Investment Tool Research Articles

Is investing in the renewable energy stock market both financially and ESG efficient? A COVID-19 pandemic analysis

The aim of this paper is to provide a tool for finding investments in the stocks of energy firms that achieve both good financial and reasonable environmental, social, and governance (ESG) performance. Our methodology entails two steps and is based on diversification-consistent DEA models. The first step involves constructing a financially efficient frontier of investment portfolios by applying the model originally proposed by Branda (Omega 52:65–76. 10.1016/j.ejor.2007.04.014, 2015). In the second step, a new DEA model is proposed in order to find the ESG-efficient portfolios among the ones already identified in the first step and to rank them with respect to their ESG performance. This model is parameterised by a weighting system that allows us to assign different importance to the various ESG outputs. Additionally, the proposal allows an evaluation of both ESG and financial efficiency related to the financial energy market over two periods (the pre-COVID-19 and COVID-19 periods), considering renewable energy and non-renewable energy firms both jointly and separately. The results support the better financial performance of the renewable energy stock market compared with that of the non-renewable energy market.

HEDGING ABILITIES of STOCKS, GOLD, and REAL ESTATE FUNDS AGAINST INFLATION DURING COVID-19: EVIDENCE FROM TÜRKİYE

The global economy is struggling with many challenges which started with the recession caused by the COVID-19 disease and continued with the high inflation problem. Investors seek safe havens in crisis periods and among many investment tools, real estate has always been an attractive tool to protect the purchasing powers of investors under high inflationary periods. By using Gultekin approach derived from the Fama-Schwert regression model, this study investigates the hedging ability of the Real Estate Investment Trusts and the Real Estate Investment Funds in Türkiye during the COVID-19 pandemic period (January 2020-December 2021). Additionally, the T-REIT Index, Istanbul Stock Exchange (BIST) 100 Index and gold (ounce/USD) and (gram/TL) were also analyzed in the study as benchmark tools. T-test, Jarque-Bera (1980), and Breusch-Pagan (1979) tests were used to validate the regression models. The results of the research revealed that only 4 of 33 T-REITs and 1 of 15 T-REIFs provided a hedge against inflation, while gold, BIST 100 and the T-REIT index did not have the statistical hedging ability during the COVID-19 period. The results of the research exhibited that, there are very few hedging tools under high inflation, and as a result, the purchasing powers of people are decreasing. Hence, the Central Bank of Türkiye Republic should seriously focus on fighting against inflation. Based on the reviewed past literature, this study seems to be the first research that seeks the hedging ability of the Turkish real estate capital market instruments in the COVID-19 period.

Diversification evidence of bitcoin and gold from wavelet analysis

To measure the diversification capability of Bitcoin, this study employs wavelet analysis to investigate the coherence of Bitcoin price with the equity markets of both the emerging and developed economies, considering the COVID-19 pandemic and the recent Russia-Ukraine war. The results based on the data from January 9, 2014 to May 31, 2022 reveal that compared with gold, Bitcoin consistently provides diversification opportunities with all six representative market indices examined, specifically under the normal market condition. In particular, for short-term horizons, Bitcoin shows favorably low correlation with each index for all years, whereas exception is observed for gold. In addition, diversification between Bitcoin and gold is demonstrated as well, mainly for short-term investments. However, the diversification benefit is conditional for both Bitcoin and gold under the recent pandemic and war crises. The findings remind investors and portfolio managers planning to incorporate Bitcoin into their portfolios as a diversification tool to be aware of the global geopolitical conditions and other uncertainty in considering their investment tools and durations.

Using Social Network Sentiment Analysis and Genetic Algorithm to Improve the Stock Prediction Accuracy of the Deep Learning-Based Approach

Traditionally, most investment tools used to predict stocks are based on quantitative variables, such as finance and capital flow. With the widespread impact of the Internet, investors and investment institutions designing investment strategies are also referring to online comments and discussions. However, multiple information sources, along with uncertainties accompanying international political and economic events and the recent pandemic, have left investors concerned about information interpretation approaches that could aid investment decision-making. To this end, this study proposes a method that combines social media sentiment, genetic algorithm (GA), and deep learning to predict changes in stock prices. First, it employs a hybrid genetic algorithm (HGA) combined with machine learning to identify chip-based indicators closely related to fluctuations in stock prices and then uses them as input for long short-term memory (LSTM) to establish a prediction model. Next, this study proposes five sentiment variables to analyze PTT social media on TSMC’s stock price and performs a grey relational analysis (GRA) to identify the sentiment variables most closely related to stock price fluctuations. The sentiment variables are then combined with the selected chip-based indicators as input to build the LSTM prediction model. To improve the efficiency of the LSTM analysis, this study applies the Taguchi method to optimize the hyper-parameters. The results show that the proposed method of using HGA-screened chip-based variables and social media sentiment variables as input to establish an LSTM prediction model can effectively improve the prediction accuracy of stock price fluctuations.

Digital Assets and the Global Economy: How the Use of Statistical Models Can Help Bitcoin Price Prediction

The purpose of the study is to analyze the potential of statistical modeling in predicting the prices of the Bitcoin cryptocurrency and its impact on the economy. In the course of the article, answers were received to such questions as: What is the impact of macroeconomic events on the dynamics of the Bitcoin price? How quickly does the cryptocurrency market stabilize after the falls? How effective is statistical modeling to solve the problem of predicting the price of Bitcoin? Which model shows the best results? What measures of regulation and control of the cryptocurrency market are necessary at the stage of its formation in the Russian Federation?Materials and methods. Historical data on average monthly Bitcoin closing prices and macroeconomic events such as the COVID-19 pandemic and the Russian-Ukrainian conflict were collected and analyzed. The paper uses statistical models, including ARIMA and LSTM, to predict future Bitcoin prices based on historical data. The accuracy of the models was calculated based on such indexes as the mean absolute error (MAE) and the mean square error (MSE). Results. Analysis of the impact of macroeconomic events showed that during the crisis, the attractiveness of Bitcoin increased and investors used this asset as a new investment tool. During the analysis of the consequences of the Russian-Ukrainian conflict for the cryptocurrency market, its reaction to geopolitical events was revealed according to the increased liquidity indexes in the market. In the process of modeling the dynamics of the average monthly Bitcoin price, the model with parameters (1, 1, 0) at MAE = 15.03% was recognized as the best ARIMA model. The LSTM neural network model on a similar data set showed a MAE error equal to 2.57%.Conclusion. The analysis shows that itcoin was the most attractive investment tool during the crisis, which led to a sharp increase in its price in 2021. The Russian-Ukrainian conflict has also affected its price, causing a significant decline in 2022. However, statistical modeling methods predict an increase in the price of Bitcoin in the first half of 2023, and governments may consider regulating or controlling its use to reduce risks associated with the cryptocurrency market. The recommended measures are the introduction of regulations, the introduction of transaction taxes, the development of national digital currencies, public education and the prevention of criminal activity.

Challenging Hedge Funds by Mimicking Their Strategies

Since the launching of the first hedge fund, its ability to have a greater return with less volatility has attracted more and more people to get involved in this investment tool. Numbers of people either invest in hedge funds, or simply simulate the hedge fund strategy in order to obtain a higher return. This paper studies whether people can beat hedge funds by simply mimicking hedge funds strategies. The results are shown by comparing data from Goldman Sachs Hedge Industry VIP ETF, which stands for GVIP, and the long-short hedge fund index from November 2016 to July 2022. According to yahoo finance, GVIP seeks to invest 80% of its assets in its underlying index, depositary receipts representing securities included in its underlying index, and in underlying stocks in turns of depositary receipts included in its underlying index. First of all, the statistical return of GVIP and the long-short hedge fund index are analyzed in terms of their mean, standard deviation, and sharpe ratio. The number of months that GVIP outperforms hedge funds, and the maximum return gap are also concluded by using cross-sectional comparison. Secondly, some regressions are applied to obtain the preliminary conclusions of this paper. Thirdly, the FamaFrench five-factor model is applied to improve the accuracy of the model, so as to further analyze and verify the previous conclusions. Based on the statistical analysis, we can conclude that investors cannot beat hedge funds by mimicking their strategies.

The Impact of Stock Selection, Market Timing and Equity Fund Size on Equity Funds Performance during Covid-19

Investors in Indonesia pay attention to the financial market's investment tools, including mutual funds. Some mutual funds even provide a higher return on investment than alternative stock investments, with a lower level of risk be-cause investment managers properly manage them. This research aims to determine how stock mutual fund success is affected by factors such as stock selection, market timing, mutual fund size, and Covid-19, with Covid-19 serving as a moderator. Purposive sampling was utilized to conduct research on Indonesian equity funds. This study's sample consists of equity funds administered by fourteen investment management firms authorized and regulated by the Financial Services Authority (OJK). Panel data regression analysis in Eviews is used to examine the information. It was discovered that neither mutual fund size nor Covid-19 played a statistically important role in determining the success of equity funds. However, equity fund returns are significantly influenced by company picking and market timing. As a supplementary benefit, Covid-19 can significantly reduce the beneficial effects of overly optimistic stock picking on the returns of equity funds. This research also demonstrates that the effects of mutual fund size and market timing on stock fund returns are not reduced by using Covid-19.

Identifying the role of green financial development played in carbon intensity: Evidence from China

Existing studies have explored the effect of green finance on carbon emissions. However, few studies have distinguished green financial efficiency from scale, which are crucial aspects for financial development. We add these dimensions to construct a synthetic index of green financial development and apply the entropy method to measure it based on China's provincial dataset from 2000 to 2019 through a series of econometric analyses. Green technology innovation, energy mix optimisation, and industrial structure upgrades are selected as the influencing channels to demonstrate the effect of green financial development on carbon intensity. The results show that in general, green financial development significantly decreases carbon intensity. All components of green finance have a positive influence in decreasing carbon intensity, except for green investment tools. Green financial efficiency has been a more important driver of carbon intensity reduction than the green financial scale, specifically for the eastern regions after the 12th Five Year Plan. The two-step analysis of the influencing mechanisms reveals that all three green transition channels are essential for decreasing the carbon intensity. These results imply more green finance policies on enhancing synergetic effect, optimising investment structure and increasing allocation efficiency are called in the future.

Islamic exchange-traded funds as a modern mechanism to revitalize the financial markets: case study of the Saudi financial market

Purpose. The present paper aims to clarify the importance of exchange-traded funds (ETFs) as one of the creative Islamic financial products that have attracted the attention of investors in recent years.
 Results. This research concluded that: (i) Islamic ETFs are a useful and convenient option for Sharia-compliant investors who prefer this type of Sharia-compliant investment, whether they are beginners or want to diversify their stock investments without the hassle of research, analysis, and selecting the right companies. The fund itself consists of several stocks selected based on the nature of the indicator that the box simulates. (ii) Islamic ETFs are distinguished by their transparency as they are affiliated with a specific market index and their investments match the components of this index, such as if the fund is linked and affected by the movement of the general index of the market or a sector index, and the fund manager may set an index for his fund in coordination with a specialized body in this field. For example, he places an index of legitimate shares, it makes it simpler for the fund's unit owners to understand how well the fund is performing by comparing it to the performance of the indicators they monitor or model. (iii) From the same perspective, the method of creating an Islamic ETF is not different from its traditional counterpart, and the main difference between them lies in the benchmark index that Islamic ETFs track. (iv) Islamic ETFs invest only in companies that comply with Sharia regulations, and in the event that any stock in the index portfolio is not compatible, it is disposed of even if the expectations of its returns are high, and thus the investment opportunities for the Islamic ETF are in a narrow space compared to the investment opportunities available for traditional ETFs, which may reduce the opportunities for the fund's investment and affect the potential return of the fund. (v) The Islamic ETF industry is weak compared to its traditional counterpart, it has been witnessing a remarkable and important growth in the recent period, especially in the Gulf region, specifically Saudi Arabia, which is one of the regions most qualified to achieve significant growth rates in the Islamic ETF sector. (vi) In general, exchange-traded funds have an important place in the financial markets, especially the Saudi stock market, as investing in these funds would expand the base of the financial markets and give investors greater flexibility in choosing how to allocate their investments across market channels and sectors.
 Scientific novelty. This study focuses on the general framework of ETFs, which aims to focus on Islamic ETFs by providing advantages and its most important dealers in addition to the mechanism of how Islamic ETFs work. Finally, the importance of Islamic ETFs in the financial markets was considered, specifically through a study of the case of the Saudi financial market, from which the study concluded that it is necessary to pay more attention to this important investment tool, although the industry of Islamic ETFs is weak compared to its counterpart; but, it has recently witnessed a remarkable and important growth, and it plays a major role in stimulating trading in the financial markets.
 Practical value. The results of this study can be used in several financial markets, taking into account everything related to compliance with Islamic law.

PENGARUH PROFITABILITAS, LIKUIDITAS, STRUKTUR MODAL DAN UKURAN PERUSAHAAN TERHADAP RETURN SAHAM PERUSAHAAN INDUSTRI BATUBARA

The development of the capital market as an investment tool institution has economic and financial functions that are increasingly needed by the public as an alternative media and fundraiser. At present capital market products are considered as an investment alternative so that this sector recorded satisfactory growth from 2015 to November 2020 reaching more than 3.6 million investors. One of the sectors listed on the Indonesia Stock Exchange (IDX) is a company engaged in the mining sector because of its role as a provider of energy resources that are very necessary for a country's economic growth. The energy sector has various sub-sectors and industries, one of which is the coal industry. This industry is one of the biggest contributors in the energy sector. Coal industry companies are companies that have contributed the most to the number of companies in the energy sector on the IDX. Of course, this absorbs many investors to invest in one of these mining companies in the hope of getting a return on the investment made. The purpose of this study was to examine the effect of profitability, capital structure liquidity and company size on stock returns. This research is a quantitative research with secondary data with a sample of 33 companies. Data analysis was carried out starting from normality, multicollinearity, autocorrelation and heteroscedasticity tests. While the other tests are multiple linear regression analysis, F test, T test and test of the coefficient of determination. The results of this study note that Profitability has no effect on stock returns, Liquidity is known to have a positive effect on stock returns, Capital structure has an effect on stock returns and Firm size has no positive effect on stock returns

INVESTMENT ACTIVITY OF THE POPULATION OF THE REPUBLIC OF KAZAKHSTAN IN THE CONTEXT OF MODERN ECONOMIC DEVELOPMENT TRENDS

The purpose of the study is to analyze current trends in the dynamics and structure of the investment activity of the population of Kazakhstan and develop recommendations for increasing the level of individual investment in order to improve the welfare of the population while increasing investment in the national economic system.The research methodology includes the use of such methods as statistical and quantitative analysis of the indicators; qualitative analysis of previous research; surveys to reveal investment opportunities and preferences of the population and graphical methods for visualizing the results of the study.Originality / value of the research. The relevance of the study lies in the systematization of data on motivation, investment tools, level and structure of the investment behavior of the population in the context of post-pandemic and global economic crisis factors. The conclusions obtained determine promising areas for increasing involvement of the population in the investment process and will contribute to further research in the fi eld of fi nancial literacy of the population, stimulating retail investment in the fi nancial and stock markets.Findings. The study revealed a tendency to shift investment preferences of the population from deposits in second-tier banks to stock market instruments and low level of investment involvement of the population compared with the indicators of developed countries. The research studies investment products that appear promising for a massive attraction of household savings to the stock and fi nancial markets and suggests directions of state stimulation of the growth of investment activity of the population.

Strategy recommender for stock options in india using deep learning and live stock data from NSE

An Option is a financial instrument, which is extensively used in share markets, money markets, and commodity markets to hedge the investment risks. It acts as financial leverage investment and is a kind of derivative instrument along with forwards, futures and swaps, which are used for managing risk of the investors. Though derivatives are theoretically risk management and leveraged investment tools, it is mostly used as speculative tools. The objectives of this work is to build a positional stock trading strategy in derivatives (Options) and recommending the users the strategy based on the predicted price of the stock, and, to generate P&L charts and Payoff graphs based on different Options strategies selected by the user. For this, the stock market data is collected from various online sources such as NSE, and BSE websites. The contributions of this work include, a comparison of various strategies and to predict the range of the stock based on Implied Volatility (IV); providing the user with the Option Chain and enable to calculate several Options indicators like PCR, Max Pain; and, the implementation of machine learning algorithm (LSTM) to predict the sentiment of the stock. An interactive web application for users is created, which provides consolidated data in the form of a dashboard from where the users can analyse the stocks and make strategic decisions on procuring stocks.

Comparison of investment in stocks, gold, and mutual funds before and during the crisis due to the Covid-19 pandemic in Indonesia

Wuhan, China was the epicenter of the virus epidemic known as COVID-19. The developing COVID-19 issue affects the finances of numerous nations, including Indonesia. The government is taking precautions against the spread of COVID-19 by encouraging people to stay home, decrease their movement, avoid crowded places, and add PSBB in 2020 and PPKM in 2021. This study compared the return and risk of stocks, gold, and mutual funds before and after the COVID-19 pandemic, which happened as a result of a crisis occurrence. The data used spans from January 2015 through December 2022 and includes the LQ45 Index's ending price as retrieved from idx.co.id, Antam's gold price as seen on Logammulia.com, and the primary NAV/unit of Manulife mutual funds as seen on Bareksa's official website, Bareksa.com. By using the One-Way ANOVA test followed by the Tukey HSD follow-up test, the result was that before the Covid-19 pandemic there were differences in returns and risks between investment in stocks, gold and mutual funds where before Covid-19 stocks were investments that had the highest returns and risks. Then, It was found that during the pandemic there were differences in returns and risks between stock, gold, and mutual fund investments, with mutual fund investments having the highest return and risk. his research is limited by the fact that only stocks, gold, and mutual funds were examined, with equity mutual fund statistics from Manulife Saham Andalan used for mutual funds. This study focuses on the time of the crisis brought on by the COVID-19 pandemic, but it is anticipated that future studies will be able to incorporate additional investment tools.

Analysis of the Factors Affecting the Price Fluctuation of Bitcoin

According to the monetary theory, this paper believes that the demand for Bitcoin mainly includes two aspects: transaction demand and investment demand. This paper further discusses the impact of different demands on the price of Bitcoin based on two aspects of demand. Transaction demand and investment demand together affect the supply and demand relationship of the Bitcoin market. The empirical results show that the volatility of Bitcoin price is higher than that of international currencies and stocks as investment tools. This article emphasizes that the price of Bitcoin is primarily affected by supply and demand.

Investor intention, investor behavior and crypto assets in the framework of decomposed theory of planned behavior.

The interest in crypto assets, known as an extremely volatile, risky and digital currency that first emerged in 2009, continued to increase day by day. Crypto assets, especially Bitcoin, have become very valuable and are now seen as an investment tool. Online survey data of 1,222 individuals were used in the study. The data were analyzed through the structural equation model. With the Decomposed Theory of Planned Behavior used in the research, the dimensions affecting Attitude, Subjective Norms, Perceived Behavioral Control variables and the effect of Intention on the behavior of investors, namely crypto asset investment, were investigated. According to the Standardized Regression Weights, a one-unit change in attitude affects intention 0.822, a one-unit change in subjective norms 0.048, and a one-unit change in perceived behavioral control affects intention 0.117. In addition, it has been concluded that the most important determinant of the actual behavior that reveals the investment is the intention with 0.754, while the PBC effect is 0.144. The study is a comprehensive study on crypto asset investments in Turkey, a developing country. It is aimed that the results obtained will contribute to researchers, crypto asset companies, policy makers and researchers who want to increase their market share in the sector.

Investments support for Sustainable Development Goal 7: Research gaps in the context of post-COVID-19 recovery

Successful achievement of the 17 Sustainable Development Goals (SDGs), including SDG 7: Affordable and Clean Energy, is impossible without proper financial support, especially after the devastating impact of the COVID-19 pandemic. Despite more than million academic papers related to SDG 7, only very few of them address the financial aspects of achieving SDG 7. To test the hypothesis, “SDG 7 related academic studies ignore the issue of investment in general and responsible investment in particular”, a meta-analysis is performed that includes a number of specific instruments and technics such as SciVal by Elsevier, VosViewer, Google trends, Google Books Ngram Viewer and Google Data. The results show a lack of appropriate academic support (methodology, empirical results, econometric models etc.) for practitioners to fill the existing financial gap and successfully achieve SDG 7. Among 1.2 million SDG 7 related papers, less than 100 deal with the financial gap problem measured by trillions of dollars in achieving SDG 7. This paper identifies the most promising and relevant topics for study related to SDG 7 and investment: the impact of the pandemic on decisions in the energy sector; efficiency of SDG 7 investment support and methodology for its assessment; green bonds, green loans, sovereign green bonds as responsible investment tools to advance SDG 7. Acknowledgments Inna Makarenko gratefully acknowledges support from the Supreme Council of Ukraine (0122U201796).The research was supported by the Scientific Grant Agency of the Ministry of Education, Science, Research, and Sport of the Slovak Republic and the Slovak Academy Sciences (VEGA), project No 1/0364/22: Research on eco-innovation potential of SMEs in the context of sustainable development.

Behavior of Banks’ Stock Market Prices during Long-Term Crises

Countries are drastically impacted by financial and fiscal crises. Financial crises have the worst impact on not only society, but also the economy. The Canadian economy underwent financial crises and recessions several times during the last century. In this paper, daily closing stock prices of five large Canadian banks were studied during the last five crisis periods. It is aimed to determine the most effective or dominant index prices on the daily closing stock price of the banks during the crisis periods. The five periods were selected from secondary data from January 1975 to December 2020 by using the graphs and the crises in the literature. Multiple linear regression was performed to analyze the impact of price indexes during crisis periods. Findings show that “price index—financials” had a positive impact on the daily closing price of banks during the last five economic crises in Canada. Since the banks have different investment tools in their portfolio, the impacts of price indexes on the daily closing prices depend on these portfolios, which ultimately could have led to the economic crises.

Long Short-Term Memory Network Hyperparameter Optimization using Hybrid Algorithm GA-PSO on LQ45 Stock Prediction

Stock is a good investment tool, keeping money from inflation, and very trendy to earn a living nowadays by becoming a trader. There is always a risk, especially when trading, because stocks can fluctuate easily depending on the company. One of the data science capabilities, prediction modeling, can help lower the risk by predicting the stock price movement. This research proposed a prediction sequential data model, an optimized hyperparameter LSTM Network using hybrid GA-PSO (LSTM-GA-PSO). Hybrid GA-PSO aims to overcome the GA problem in terms of slow execution time and PSO that tend to be trapped in the local optimum. With the characteristics of both algorithms, the hybrid algorithm can solve each other algorithms downside. The low fluctuation stock of the Indonesian Index LQ45 dataset will be used to train and test the model and compare the proposed model with LSTM-GA and LSTM-PSO. Experiment results show that the hybrid LSTM-GA-PSO has a promising performance. Hybrid GA-PSO improved 18.18% of its time execution to GA and 29.07% accuracy to PSO.

Cryptocurrencies and regulations: a comparative framework for international implementations

Purpose- The recent developments in technology have created a remarkable increase in the financial markets. The decentralization of crypto assets and the price movements attract investors attention as an demanding financial instrument. Since the beginning of pandemics, inflation is one of the major macroeconomic issue in the globe that push the investors to seek for new investment opportunities. Perhaps the positive perception regarding the cryptocurrency investment is its protection from inflation. In addition cost-effective mode of transaction and easy transfer of funds make these instruments unique. On the other hand, it can also lead to unsolicited consequences such as money laundering, illegal purchases, and the elimination of corruption. In this context, regulations are being formed to bring crypto assets, which attract the attention of experts, into compliance with the tax and trade-related laws of countries in the financial system. In this study, it is aimed to convey the importance of regulation and regulations on the world. Methodology- Since the first launch of Bitcoin as a cryptocurrency in 2009; the recent discussion came forward on how to regulate this market. Understanding cryptocurrency takes time and effort while they are extremely volatile investment. The crypto money applications of the countries and their taxation and approaches towards these applications have been evaluated by examining the official reports of the countries. Findings- Countries' perspectives on crypto money, the concept and definitions of crypto money vary. Some accept the cryptocurrencies as legal investment tools and draw a legal framework, while some announce that they eliminate these investments. Perhaps developing a framework can help to regulate both actors and also the transactions in the crypto ecosystem. National authorities plan to take a position how technology can be used to create cryptoassets. Conclusion- Regulations are important for making the financial system safe, protecting individual investors and ensuring an orderly environment in enterprises. Countries need to accept the crypto currency system and keep up with the innovations of crypto money by changing the current standards if necessary. Keywords: Regulation, crypto asset, ındividual ınvestor, tax JEL Codes: D53, F47, G11

An explanatory analysis on the investment preferences of university students with respect to financial characteristics and crypto assets

Purpose- Considering the diversity of investment instruments and the complexity of financial markets, making the right investment decisions is an important issue for market participants. In addition, rational and irrational factors have an impact on investment decisions. In this context, determining the effect of rational and irrational factors, which can be considered as financial characteristics for an investor, on investment preferences will be beneficial for future investments. The aim of this study is to determine how financial characteristics (financial literacy, self-confidence, risk susceptibility) shape investment preferences by using a sample selected from university students. Another aim of the study is to determine whether investment decisions for crypto assets, which can be considered relatively new compared to traditional assets, are also shaped according to these characteristics. Methodology- The data obtained in the study were evaluated by frequency analysis and correlation analysis, respectively. Findings- While the most preferred investment tool by female participants is gold, men are in favor of foreign currency. Cryptoassets are the second choice of male participants. Equity and deposit investment were the least preferred products for both genders. The averages of financial literacy, self-confidence and risk-proneness of male participants are higher than females. When the correlation levels between the investment preferences of the participants are examined, it is seen that the most striking correlation is between cryptoassets and stocks. On the other hand, it was seen that the participants who invested more in cryptoassets were investors with cryptoasset literacy and risk-taking profiles. Finally, it is another finding that male participants prefer to invest in crypto assets approximately three times more than female participants. Conclusion- The results of the study revealed that the investment preferences of university students differ according to various criteria. Keywords: Investment preferences, financial literacy, self-confidence, risk susceptibility, cryptoassets JEL Codes: G11, G19