Profitable Investment Strategies Research Articles

Optimizing investment portfolios with a sequential ensemble of decision tree-based models and the FBI algorithm for efficient financial analysis

This research presents a comprehensive, sequential ensemble framework meticulously crafted for optimizing investment portfolios, focusing on the construction industry. It employs decision tree-based and metaheuristic optimization algorithms to create an efficient stock-selection framework grounded in financial analysis. This approach offers a profitable investment strategy integrated with portfolio optimization while systematically lowering the portfolio investment threshold. In this framework, a web crawler was deployed to gather daily closing prices of stocks from the Taiwan Stock Exchange, along with monthly revenue data and financial statements. Subsequently, decision tree-based models were utilized to pinpoint the fundamental financial indicators with significant explanatory power over revenues. The stock selection conditions aligned with these indicators were optimized through a newly developed metaheuristic algorithm named the forensic-based investigation (FBI) optimizer. The optimal conditions were subsequently integrated with the equal-weighting scheme, mean-variance method, and hierarchical risk parity to identify the most effective investment portfolio strategies. Backtesting results showed that the proposed stock-portfolio investment strategy, optimized through machine learning and a metaheuristic algorithm, is well-suited for construction and all-stock categories. This study equips professional investment advisors or securities investment institutions with a decision-aid expert system for initial stock selection.

Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities

Accurate predictions of stock markets are important for investors and other stakeholders of the equity markets to formulate profitable investment strategies. The improved accuracy of a prediction model even with a slight margin can translate into considerable monetary returns. However, the stock markets' prediction is regarded as an intricate research problem for the noise, complexity and volatility of the stocks' data. In recent years, the deep learning models have been successful in providing robust forecasts for sequential data. We propose a novel deep learning-based hybrid classification model by combining peephole LSTM with temporal attention layer (TAL) to accurately predict the direction of stock markets. The daily data of four world indices including those of U.S., U.K., China and India, from 2005 to 2022, are examined. We present a comprehensive evaluation with preliminary data analysis, feature extraction and hyperparameters' optimization for the problem of stock market prediction. TAL is introduced post peephole LSTM to select the relevant information with respect to time and enhance the performance of the proposed model. The prediction performance of the proposed model is compared with that of the benchmark models CNN, LSTM, SVM and RF using evaluation metrics of accuracy, precision, recall, F1-score, AUC-ROC, PR-AUC and MCC. The experimental results show the superior performance of our proposed model achieving better scores than the benchmark models for most evaluation metrics and for all datasets. The accuracy of the proposed model is 96% and 88% for U.K. and Chinese stock markets respectively and it is 85% for both U.S. and Indian markets. Hence, the stock markets of U.K. and China are found to be more predictable than those of U.S. and India. Significant findings of our work include that the attention layer enables peephole LSTM to better identify the long-term dependencies and temporal patterns in the stock markets' data. Profitable and timely trading strategies can be formulated based on our proposed prediction model.

Impact of Google searches and social media on digital assets’ volatility

Advanced digitalization and financial technology have of recent times become among the most crucial tools. Data mining and sentiment analysis have revealed the importance of digitalization in modern times. This study examines the influence of Google search activity on the volatility of digital assets. We analyzed six digital asset prices for Bitcoin, Bitcoin Cash, Ethereum, Ethereum Classic, Litecoin, and Ripple from the Coinmarketcap database. We used tweets on Twitter to survey users’ sentiment by using the Twitter search Application Programming Interface and Google trend search from web searches, news searches, and YouTube searches data using RStudio software. The study spanned 1 September 2019 to 31 January 2020 and employed the Vector Autoregression (VAR) approach for analysis. The VAR estimation revealed that Google search variables have significantly influenced the volatility of Bitcoin, Ethereum, Litecoin, and Ripple, as supported by the Granger causality test and impulse response function. The results of this study could be useful for investors and policymakers in drawing up strategies to reduce market volatility. These results should thus be useful to investors in developing profitable investment strategies to mitigate the impact of market turbulence.

Examining the efficiency of stock markets using multifractal detrended fluctuation analysis. Empirical evidence from OIC (Organization of Islamic Cooperation) countries during the GFC and COVID-19 pandemic

PurposeThis study aimed to examine the efficient market hypothesis (EMH) for the stock markets of 12 member countries of the Organization of Islamic Cooperation (OIC), such as Egypt, Indonesia, Jordan, Kuwait, Malaysia, Morocco, Pakistan, Saudi Arabia, Tunisia, Turkey and the United Arab Emirates (UAE), during the global financial crisis (GFC) and the COVID-19 (CV-19) epidemic. The objective was to classify the effects on individual indices.Design/methodology/approachThe study employed the multifractal detrended fluctuation analysis (MF-DFA) on daily returns. After calculation and analysis, the data were then divided into two significant events: the GFC and the CV-19 pandemic. Additionally, the market deficiency measure (MDM) was utilized to assess and rank market efficiency.FindingsThe findings indicate that the average returns series exhibited persistent and non-persistent patterns during the GFC and the CV-19 pandemic, respectively. The study employed MF-DFA to analyze the sequence of normal returns. The results suggest that the average returns series displayed persistent and non-persistent patterns during the GFC and the CV-19 pandemic, respectively. Furthermore, all markets demonstrated efficiency during the two crisis periods, with Turkey and Tunisia exhibiting the highest and deepest levels of efficiency, respectively. The multifractal properties were influenced by long-range correlations and fat-tailed distributions, with the latter being the primary contributor. Moreover, the impact of the fat-tailed distribution on multifractality was found to be more pronounced for indices with lower market efficiency. In conclusion, this study categorizes indices with low market efficiency during both crisis periods, which subsequently affect the distribution of assets among shareholders in the stock markets of OIC member countries.Practical implicationsMultifractal patterns, especially the long memory property observed in stock markets, can assist investors in formulating profitable investment strategies. Additionally, this study will contribute to a better understanding of market trends during similar events should they occur in the future.Originality/valueThis research marks the initial effort to assess the impact of the GFC and the CV19 pandemic on the efficiency of stock markets in OIC countries. This undertaking is of paramount importance due to the potential destabilizing and harmful effects of these events on global financial markets and societal well-being. Furthermore, to the best of the authors’ knowledge, this study represents the first investigation utilizing the MFDFA method to analyze the primary stock markets of OIC countries, encompassing both the GFC and CV19 crises.

(Re‐)Imag(in)ing Price Trends

ABSTRACTWe reconsider trend‐based predictability by employing flexible learning methods to identify price patterns that are highly predictive of returns, as opposed to testing predefined patterns like momentum or reversal. Our predictor data are stock‐level price charts, allowing us to extract the most predictive price patterns using machine learning image analysis techniques. These patterns differ significantly from commonly analyzed trend signals, yield more accurate return predictions, enable more profitable investment strategies, and demonstrate robustness across specifications. Remarkably, they exhibit context independence, as short‐term patterns perform well on longer time scales, and patterns learned from U.S. stocks prove effective in international markets.

Asymmetric and Lag Effects of Industry Risk Factors on the Malaysian Oil and Gas Stocks

This study examines asymmetric and the lag effects of oil price, gas price, and exchange rate on stock performance of the Malaysian oil and gas sub-industries. Using company-level data and multi factor asset pricing models, this study found that oil price, gas price, exchange rate, and common systematic risk factors such as market, size, and book-to-market have significant effects on the stock performance of Malaysian oil and gas sub-industries, but these exposures remain heterogeneous. Oil price and exchange rate show strong asymmetrical impacts on stock performance when decomposed into oil price increases (decreases) and exchange rate depreciation (appreciation). Increases in oil price and exchange rate depreciation have a greater impact on stock performance of oil and gas industry than decreases in oil price and exchange rate appreciation. Oil price, gas price, and exchange rate exhibit lag effects on the performance of oil and gas stocks. The findings have important asset pricing implications for profitable investment and portfolio strategies based on oil and gas risk factors for oil-exporting country.JEL Classifications: C13; C58; G10; G12; G14; Q4

Explaining the Gamestop Short Squeeze using Ιntraday Data and Google Searches.

This article examines the recent short squeeze of the GameStop (GME) stock in early 2021. This event, although not the only case of short squeeze, has some idiosyncratic features that makes it extremely interesting, mainly because it was organized by non-institutional investors through social media like Reddit. Using intraday data during the period 4/1/2021-26/3/2021, we conclude that volume and Google searches provide useful information which enable us to explain the GME performance. Moreover, we show that information on volume and Google searches can provide investors with valuable data, but the faster investors have access to this information, the greater the advantages. This analysis could be very useful for scholars and practitioners who examine profitable investment strategies when such conditions emerge in the markets, and it also provides some thoughts for regulators regarding the impact of networks, social or not, on the stability of the financial markets.

The footprints of Russia–Ukraine war on the intraday (in)efficiency of energy markets: a multifractal analysis

PurposeThis paper examines the impact of Russian invasion of Ukraine on the intraday efficiency of four major energy markets, namely, diesel oil, Brent oil, light oil and natural gas.Design/methodology/approachThis study applies the multifractal detrended fluctuation analysis (MFDFA) to high-frequency returns (30-min intervals) for the period from October 21, 2021, to May 20, 2022. The data sample of 5,141 observations is divided into two sub-samples, before and after the invasion of 24th February 2022. Additionally, the magnitude of long memory index is employed to investigate the presence of herding behavior around the invasion period.FindingsResults confirm the presence of multifractality in energy markets and reveal significant changes of multifractal strength due to the invasion, indicating a decline of intraday efficiency for oil markets. Surprisingly, the natural gas market, being the least efficient before the invasion, turns out to be more efficient after the invasion. The findings also suggest that investors in these energy markets are likely to show herding, more prominently after the invasion.Practical implicationsThe multifractal patterns, in particular the long memory property of energy markets, can help investors develop profitable investment strategies. Furthermore, the improved efficiency observed in the natural gas market, after the invasion, highlights its unique traits and underlying complexity.Originality/valueThis study is the first attempt to assess the impact of the Russia–Ukraine war on the efficiency of global commodity markets. This is quite important because the adverse effects of the war on financial markets may potentially cause destabilizing outcomes and negative effects on social welfare on a global scale.

Changing the Landscape of an American Town: Immigrantrification of a Korean Ethnoburb and Its Cultural and Economic Consequences.

This study focuses on the social, cultural, and physical transformations—referred to as immigrantrification (gentrification by immigrants)—initiated by Korean immigrants in Palisades Park, a well-known Koreatown in New Jersey, in the first two decades of the twenty-first century. It draws on data collected from ethnographic field research conducted in 2019–2020, including 67 interviews. Gentrification initiated as a profitable investment strategy of middle-class, entrepreneurial Korean immigrants has followed the town’s revitalization brought about by the population and economic growth and the expansion of amenities during the establishment of the Korean ethnoburb. While many residents complain about overcrowding, pollution, land-use intensity, and parking problems, Guatemalan immigrants—active participants in labor-intensive gentrification who have established their sub-enclave, gradually supplanting White residents—express fear of being priced out of the immigrantrified town. Thus, this study addresses two issues underrepresented in the gentrification discourse — third-world immigration and ethnic minority gentrification — by expanding the research on Korean gentrifiers.

Can financial strength indicators form a profitable investment strategy? The case of F-Score in Europe

Research Question: Can the F-Score predict the stock market returns in the cross section of international stock markets? Motivation: The majority of the literature, in the area of the F-Score metric, has examined whether it can be used to predict future financial profitability, the relationship of F-Score with book-to-value metrics and the momentum premium and whether it can be used as a successful investment strategy tool. There only three studies that examine the relationship between the F-Score and future stock returns, without the use of complementary variables, and in other countries except Europe. This paper seeks to fill this gap. Data: The dataset of the present research consists of listed European companies from 21 countries (in random order: Finland, United Kingdom, Switzerland, Turkey, Hungary, Portugal, Spain, Poland, Norway, Luxembourg, Italy, Netherlands, Ireland, Greece, Belgium Germany, Denmark, France Czech Republic, Sweden, Austria), from 1989 to 2016. We collect firm-level accounting information as provided by Worldscope, as well as the monthly total returns for common stocks from Datastream. Tools: With the use of a dataset consisting of European companies from 21 countries, portfolio analysis and time series regressions are performed using abnormal monthly returns (monthly returns minus risk-free interest rates). Findings: We find that the F-Score is a statistically significant predictor as well as an economically meaningful index. Its performance forecasting ability is visible in developed Europe, both in small and large companies, and remains stable after controlling for established cross-sectional determinants (such as book market, investment, and company size). Contribution: This study seeks to fill the gap in the stock return and F-Score relationship in a European setting controlling for the other financial variables. Our empirical models are tested across a number of different economic and stock market backgrounds and the implications of our results are of particular interest for academics, for investors (retail and institutional) and for policy makers.

Detecting the lead–lag effect in stock markets: definition, patterns, and investment strategies

Human activities widely exhibit a power-law distribution. Considering stock trading as a typical human activity in the financial domain, the first aim of this paper is to validate whether the well-known power-law distribution can be observed in this activity. Interestingly, this paper determines that the number of accumulated lead–lag days between stock pairs meets the power-law distribution in both the U.S. and Chinese stock markets based on 10 years of trading data. Based on this finding this paper adopts the power-law distribution to formally define the lead–lag effect, detect stock pairs with the lead–lag effect, and then design a pure lead–lag investment strategy as well as enhancement investment strategies by integrating the lead–lag strategy into classic alpha-factor strategies. Tests conducted on 20 different alpha-factor strategies demonstrate that both perform better than the selected benchmark strategy and that the lead–lag strategy provides useful signals that significantly improve the performance of basic alpha-factor strategies. Our results therefore indicate that the lead–lag effect may provide effective information for designing more profitable investment strategies.

Application and Comparison of Multiple Machine Learning Models in Finance

Accurate and effective financial data analysis is very important for investors to avoid risks and formulate profitable investment strategies. Therefore, the analysis of financial data has important research significance. However, the financial market is a complex nonlinear dynamic system affected by many factors. It is very challenging to analyze the financial data according to the obtained information. Among them, stock selection is the most typical financial data mining problem. The core of stock selection is to design a systematic scoring mechanism to quantitatively score stocks so as to more intuitively reflect the investment value of stocks. The scoring mechanism is based on the assumption that stocks with higher scores have higher investment value and stocks with lower scores have lower investment value. The stock selection model proposed in this paper mainly includes two steps: stock prediction and stock scoring. First, construct stock predictors and use machine learning forecasting methods to predict the future price of each stock. Second, construct a stock scoring mechanism to evaluate each stock through the predictive factors and financial factors in the previous step. Finally, select high-scoring stocks and make equal-weight investments. This paper applies the model to the empirical study of the A-share market, verifies its feasibility and effectiveness, and makes a systematic comparison with other benchmark models.

Investment Strategy Based on Bias Behavior and Investor Sentiment in Emerging Markets

This research's main objective is to select profitable investment strategies with the presence of sentiment investors in emerging markets, with behavior bias-based portfolio methods. The sample of 114 companies traded daily on IDX was conducted over three years with weekly data. This study uses pairwise comparison and OLS. The research results show that contrarian strategies are more profitable than momentum. Investors benefit when mild conditions are optimistic and more significant when the conditions are pessimistic. This research proves that investor sentiment in the market can distort investor investment decisions, even using the behavior-bias method. Therefore, forming a portfolio will be more appropriate based on biased behavior because it facilitates investment decision-making.How to Cite:Koesoemasari, D.S.P., Haryono, T., Trinugroho, I., Setiawan, D. (2022). Investment Strategy Based on Bias and Investor Sentiment in Ememerging Market. Etikonomi, 21(1), 1-10. https://doi.org/10.15408/etk.v21i1.22290.

Betting against real earnings management

PurposeThe study presents the zero investment strategies based on the pricing impact of real earnings management (REM) on stock returns after taking into account the direction and endogeneity nature of REM.Design/methodology/approachThe authors use standard portfolio methodology and Fama–Macbeth cross-sectional regression to analyze the data for this study. Both upward and downward form of REM has been examined. Accrual earnings management (AEM) has been controlled while examining the association between REM and stock returns.FindingsThe findings demonstrate that the REM anomaly exists in the Indian equity market and is consistent under different market conditions and investment horizons. It is robust after controlling for cross-sectional effects and AEM. Our subsequent analysis suggests that a decile-based zero investment portfolio strategy based on REM loadings generates an annual excess return of 17.90%. The presented annual excess return is highest among quantile and mean-based investment strategies. Further, the authors find that REM sorted proposed investment strategies outperform the AEM sorted investment strategies in all spheres.Practical implicationsThe findings suggest that investors can form an arbitrage profitable investment strategy by taking a long position in the bottom 10% of negative REM stocks, and a short position in the top 10% of positive REM stocks.Originality/valueThis is the first study that examines the pricing impact of REM on stock returns and provides zero investment strategies by betting against REM.

Finance is Not Excused: Why Finance Should Not Flout Basic Principles of Statistics

Several features of financial research make it particularly prone to the occurrence of false discoveries. First, the probability of finding a positive (profitable investment strategy) is very low, due to intense competition. Second, true findings are mostly short-lived, as a result of the non-stationary nature of financial systems. Third, unlike in the natural sciences, it is rarely possible to verify statistical findings through controlled experiments. Finance’s inability to conduct controlled experiments makes it virtually impossible to debunk a false claim. One would hope that, in such a field, researchers would be particularly careful when conducting statistical inference. Sadly, the opposite is true. Tenure-seeking researchers publish thousands of academic articles that promote dubious investment strategies, without controlling for multiple testing. Some of those articles are written for, funded, or promoted by investment firms with a commercial interest. As a consequence, today’s academic finance exhibits some resemblance with medicine’s predicament during the 1950-2000 period, when Big Tobacco paid for thousands of studies in support of their bottom line. Unlike finance, medical journals today impose strict controls for multiple testing. Academic finance’s denial of its replication crisis risks its branding as a pseudoscience.

Cross-cryptocurrency Return Predictability

Using the minute-frequency data of the top 30 coins listed on Binance, which represent 86% of the total dollar trading volume of the cryptocurrency market, we document strong evidence of cross-cryptocurrency return predictability. The lagged returns of other cryptocurrencies serve as significant predictors of focal cryptocurrencies up to ten minutes without return reversals, in line with gradual information diffusion. To alleviate statistical issues, we employ the adaptive LASSO method to identify informative predictors and find consistent results. Furthermore, even after accounting for the highest transaction costs on Binance, a long-short portfolio formed based on our findings with a ten-minute rebalancing frequency generates a significant out-of-sample return of 1.14 bps on a minute basis for the futures trading, indicating a profitable investment strategy.

What Drives Excess Trading during the COVID-19 Pandemic?

Manuscript type: Research paperResearch aims: This study aims to examine the underlying psychological and sociological factors that drive excess trading in the Malaysian stock market during a global health crisis such as the COVID-19 pandemic. Design/Methodology/Approach: A self-administered online questionnaire was collected from 271 individual investors to examine the association between big-five personality traits and trading frequency. Demographic information and investment behaviours of investors were also included in the study. The multinomial logit regression model was used to test the research hypotheses. Research findings: Findings show that personality traits such as openness to experience and agreeableness have a significant influence on trading frequency. Demographic factors and investment behaviours such as gender, household income level, years of investment experience and type of investor all have a significant positive relationship with trading frequency. Theoretical contribution/Originality: This study contributes to the current investor behaviour literature in Malaysia, which remains to be very limited, especially during a global health crisis. The study indicates that personality traits, demographic, socio-economic factors, and investment behaviours affect the trading frequency of Malaysian. Practitioner/Policy implication: This study offers insights for financial institutions and individual investors on the type of personality traits, demographic, socio-economic factors, and investment behaviours that drive excess trading during a global health crisis. The findings provide important contributions to avoid serious mistakes in investment analysis and trading profitable investment strategies, thus improving individual and team performance. Research limitation/Implications: Some results are not significant and may be limited due to the small sample size used in this study. Future research could recruit more retail investors to confirm the significance level of those variables. Besides, the study can be conducted after the COVID-19 pandemic to explore whether there is any significant difference in the variables during and after the global health crisis.

Equity market anomalies in major European economies

This paper investigates five leading equity market anomalies – size, value, momentum, profitability, and asset growth, for four Western European markets, namely, Germany, France, Italy and Spain, from January 2002 to March 2018. The study tests whether these anomalies reverse under different macro-economic uncertainty conditions, and evaluates if strategies based on time diversification can be formed using these equity market anomalies. Market anomalies were tested using four major asset pricing models – the Capital Asset Pricing Model, the Fama-French three-factor model, the Carhart model, and the Fama-French five-factor model. Macro-economic uncertainty was tested using two proxies, namely VIX and default premiums. Time diversified strategies were examined by estimating Sharpe ratios of combined portfolios formed by combining winner univariate portfolios. Value effect in Germany, Size effect in France and Profitability effect in Italy and Spain provide the highest unadjusted returns on long side strategies. No significant reversal of these anomalies was found under different macroeconomic uncertainties. Asset pricing tests show that CAPM works well for Spain and Italy, while Carhart’s model explains returns in Germany. The Fama-French five factor model does not seem to be a good descriptor of asset pricing for data. No suitable model for explaining asset returns is identified for France. Finally, it is observed that some of the equity market anomalies seem to be countercyclical and therefore provide time diversification opportunities. The study has implications for academicians, investors, and policy makers by providing insights for developing profitable investment strategies and highlighting the efficacy of alternative models as performance benchmarks.

A review of machine learning experiments in equity investment decision-making: why most published research findings do not live up to their promise in real life

The numerical nature of financial markets makes market forecasting and portfolio construction a good use case for machine learning (ML), a branch of artificial intelligence (AI). Over the past two decades, a number of academics worldwide (mostly from the field of computer science) produced a sizeable body of experimental research. Many publications claim highly accurate forecasts or highly profitable investment strategies. At the same time, the picture of real-world AI-driven investments is ambiguous and conspicuously lacking in high-profile success cases (while it is not lacking in high-profile failures). We conducted a literature review of 27 academic experiments spanning over two decades and contrasted them with real-life examples of machine learning-driven funds to try to explain this apparent contradiction. The specific contributions our article will make are as follows: (1) A comprehensive, thematic review (quantitative and qualitative) of multiple academic experiments from the investment management perspective. (2) A critical evaluation of running multiple versions of the same models in parallel and disclosing the best-performing ones only (“cherry-picking”). (3) Recommendations on how to approach future experiments so that their outcomes are unambiguously measurable and useful for the investment industry. (4) An in-depth comparison of real-life cases of ML-driven funds versus academic experiments. We will discuss whether present-day ML algorithms could make feasible and profitable investments in the equity markets.

Regime switches and commonalities of the cryptocurrencies asset class

In this paper we test for regime changes and possible regime commonalities in the price dynamics of Bitcoin, Ethereum, Litecoin and Monero, as representatives of the cryptocurrencies asset class. Several parametric models are considered for the joint dynamics of the basket price where parameters are modulated through a Hidden Markov Chain with finite state space. Best specifications within Gaussian and Autoregressive models for price differences are selected by means of the AIC and BIC information criteria and through an out-of-sample forecasting performance. The empirical results, within the period January 2016 to October 2019, suggest that three or four states may be relevant to describe the dynamics of each individual cryptocurrency, depending on the selection criteria, while the entire basket displays at most three common states. Finally, we show how the identification of appropriate models may be exploited in order to build profitable investment strategies on the considered cryptocurrencies.