Past Prices Research Articles

Robust Stock Price Prediction using Gated Recurrent Unit (GRU)

Forecasting the direction of price movement of the stock market could yield significant profits. Traders use technical analysis, which is the study of price by scrutinizing past prices, to forecast the future price of the nickel stock price. Therefore, in this study, we propose Gated Recurrent Units (GRU) to predict nickel stock price trends. This research aims to produce an accurate nickel stock price trend prediction model. The research method utilized historical data on nickel stock prices from Yahoo Finance. The research results show that the model developed accurately predicted nickel stock price trends. From the RMSE, MAE, and MSE analysis results, the RMSE value was 0.0123, the MAE value was 0.0089, and the MSE value was 0.0002 on the test data.

Analysing the financial landscape of European households: Insights from panel data

This article makes a significant contribution to the existing body of research concerning the subjective evaluations of household financial conditions and the objective analysis of economic circumstances and savings potential within households. The study elucidates the intricate relationships between subjective assessments of household financial situations, broader economic conditions, price trends, and savings potential. Drawing upon data pertaining to households' self-assessments of their current and future financial outlook, this paper undertakes a comprehensive examination of disparities across several European nations. The initial cross-sectional survey study encompassed all member countries of the European Union and Great Britain. The study spanned a timeframe of 32 months, from January 2018 to August 2020, divided into two distinct sub-periods: one preceding the onset of the pandemic and the other during its occurrence. Employing panel models, this research identifies factors significantly influencing subjective evaluations of household financial well-being. The estimations of model parameters during the pandemic period revealed noteworthy trends: assessments of household finances exhibited considerably greater consistency than those observed in the pre-pandemic era. Across both sub-periods, the findings consistently underscored a significant and positive correlation between the evaluation of the overall economic situation within the country and the potential for savings and the subjective assessment of household financial conditions. However, the findings from the pandemic period failed to corroborate a substantial link between assessments of past and future price trends and their impact on household financial evaluations. Furthermore, the regression coefficients within the models describing future financial evaluations demonstrated a pronounced increase when considering the dependent variable as the financial assessment of the preceding year. Given that household opinions hold paramount importance as target variables for economic policies, the investigation into the ramifications of subjective evaluations of household financial situations remains particularly pertinent. These evaluations can exert both direct influence, such as on household welfare, and indirect effects by guiding the formulation of pertinent financial instruments by institutions within the financial sector.

Housing market dynamics of the post-Sandy Hudson estuary, Long Island Sound, and New Jersey coastline are explained by NFIP participation

How flooding affects home values can determine the path of economic recovery for communities and have lasting impacts on national and global financial systems. Yet, our understanding of how flood insurance, community risk perception, and past flooding events shape future housing prices (HPs) remains limited. To explore this, we used a socio-environmental (SE) model and studied the temporal impacts of flooding on mean housing values across 496 coastal census tracts of New York, Connecticut, and New Jersey, US, from 1970 to 2021. The modeling exercise demonstrated that the initial economic impact of Hurricane Sandy was largely absorbed by the National Flood Insurance Program (NFIP); however, the region then exhibited a long-term decline in home values, which was well described by an interrupted time series model. We found significant correlations between SE model parameters describing HP change and those describing tract-scale behaviors and perceptions, suggesting that the salience of past flooding events and NFIP participation may be important regional drivers of HPs. Tracts with greater post-flood change in active insurance policies exhibited larger decreases in mean home values than those with more stable NFIP participation. An improved understanding of relationships between HPs, flood insurance, and community perceptions could support more equitable distributions of resources and improved policy interventions to reduce flooding risk.

STUDY OF WEAK FORM OF MARKET EFFICIENCY IN INDIA

A securitys current market price already accounts for all of its prior trading volumes and prices, according to Indias lax form of market efficiency. An investor cannot generate spectacular returns by only relying on the analysis of previous price movements because any relevant information about the security would already be reflected into its present price. The accuracy and promptness with which prices reflect information about the market is known as market efficiency. All the information from prior prices and traded volume is included in the weak version of the markets current prices. Furthermore, future prices cannot be predicted by looking at past prices. Everyone has access to past prices, even though some people can obtain them more easily than others. Liquidity traders may sell their stocks, generating price volatility, without considering the shares fundamental value. The market price reflects the intrinsic value as a result of the buying and selling of information traders. The filter rule, runs test, and serial correlation are used to assess market efficiency. Run tests have been used in this research to identify market efficiency. Information on stock prices for the selected companies was gathered from the National Stock Exchange (NSE).

Dynamic modelling of price expectations and judgments

Official data about consumers’ qualitative expectation and perception of inflation are derived from repeated surveys conducted by national statistical institutes. In EU, these data are published in aggregate form, and cannot be described by means of classical methods based on cumulative models for ordinal data. This article illustrates an integrated approach that locates CUB mixture models for ratings in a time series perspective in order to investigate the joint evolution of inflation judgments and expectations in Italy. In order to measure the common sentiment of interviewees through the feeling component of the model, net of possible uncertainty and nuisance effects, its estimation is pursued through profile likelihood methods given the empirical frequency distributions of consumers’ opinions observed over time. Then, the relationship between the time series of the estimated feeling parameters is modelled using a dynamic regression model and the results are compared in three periods marked by different economic conditions. Results indicate that each series has a substantial inertial component, and thus it is characterized by a slow variation over time, and that both judgments about past price levels and previous expectations affect current expectations about the future in fairly different ways for the three time periods.

Portfolio optimization using predictive auxiliary classifier generative adversarial networks

In financial engineering, portfolio optimization has been of consistent interest. Portfolio optimization is a process of modulating asset distributions to maximize expected returns and minimize risks. Despite numerous studies on shallow learning models, they have shown limited success in analyzing the complex nature of massive stock data, a task where recent deep learning models excel. However, the deterministic nature of conventional deep learning models impedes their consideration of portfolio risk due to an inherent lack of uncertainty quantification in their predictions. This paper proposes a novel portfolio weighting strategy, incorporating both risk and return considerations within a deep learning framework. We propose the Predictive Auxiliary Classifier Generative Adversarial Networks (PredACGAN), a probabilistic deep learning model, to measure prediction uncertainty. The PredACGAN generator leverages latent vectors and historical stock prices to predict future returns. The model synthesizes predictive distributions from various latent vectors and past prices. The associated risk is produced via the entropy of these distributions, facilitating portfolio optimization through both return and risk considerations. The proposed algorithm removes high-risk assets from the investment universe at rebalancing moments, enabling PredACGAN to optimize portfolios considering both return and risk. We evaluated PredACGAN and the accompanying algorithm with S&P 500 stocks from 1990 to 2020, with portfolios rebalanced monthly based on PredACGAN predictions and risk measures. The PredACGAN portfolios yielded 9.123% annual returns and a 1.054 Sharpe ratio, outperforming a risk-agnostic portfolio yielding 1.024% annual returns and a 0.236 Sharpe ratio. The PredACGAN portfolio also exhibited lower maximum drawdowns, highlighting its effectiveness.

Implementasi Metode Fuzzy Time Series Untuk Meramalkan Jumlah Ekspor Produk Kopi Dari Indonesia

The current trade era opens up opportunities for trading Indonesian coffee in the international market. The number of future exports can be predicted by knowing past export prices. The method that can be used to predict the number of Indonesian coffee exports to various countries is the Fuzzy Time Series method. The Fuzzy Time Series method, the forecasting technique, utilizes fuzzy sets as the basis for the prediction process. In this study, the forecasting stages include defining Xmax and Xmin, determining how many and how long class intervals are, defuzzification and defining fuzzy sets, fuzzifying data on the number of coffee exports, determining fuzzy logic relationships (FLR), establishing fuzzy logical groups (FLRG) relationships, defuzzification, and perform forecasting calculations. Using the Fuzzy Time Series Method, the results of forecasting Indonesia's coffee exports for the following year are 376371.3 tons of coffee products to be exported so that exporters can see this opportunity Keywords: Coffee, export, forecasting, fuzzy time series

Distributed Dynamic Pricing Strategy Based on Deep Reinforcement Learning Approach in a Presale Mechanism

Despite the emergence of a presale mechanism that reduces manufacturing and ordering risks for retailers, optimizing the real-time pricing strategy in this mechanism and unknown demand environment remains an unsolved issue. Consequently, we propose an automatic real-time pricing system for e-retailers under the inventory backlog impact in the presale mode, using deep reinforcement learning technology based on the Dueling DQN algorithm. This system models the multicycle pricing problem with a finite sales horizon as a Markov decision process (MDP) to cope with the uncertain environment. We train and evaluate the proposed environment and agent in a simulation environment and compare it with two tabular reinforcement learning algorithms (Q-learning and SARSA). The computational results demonstrate that our proposed real-time pricing learning framework for joint inventory impact can effectively maximize retailers’ profits and has universal applicability to a wide range of presale models. Furthermore, according to a series of experiments, we find that retailers should not neglect the impact of the presale or previous prices on consumers’ purchase behavior. If consumers pay more attention to past prices, the retailer must decrease the current price. When the cost of inventory backlog increases, they need to offer deeper discounts in the early selling period. Additionally, introducing blockchain technology can improve the transparency of commodity traceability information, thus increasing consumer demand for purchase.

Trend information and cross-sectional returns: The role of analysts

This study investigates the role of analysts on the relationship between trend information and cross-sectional returns. Our results show that the trend information about past price and trading volume significantly predicts future stock returns in the Chinese stock market. The trend effect is more pronounced among stocks without analyst coverage or low analyst coverage, which indicates that analysts play an important role in disseminating information and helping information to be incorporated into stock price more quickly, thus reducing mispricing and weakening the trend effect. Considering the information's updates from analysts, the findings suggest that the trend effect is stronger for stocks with no revision, while the trend effect becomes weaker or even disappears for stocks with upgrade or downgrade revision. Moreover, our results show that analyst recommendations fail to take full advantage of trend information or even contradict trend signals although analyst recommendations do contain forecast information related to future stock returns. Furthermore, we find that analyst recommendations provide additional useful information that can enhance the predictability of trend information, but the value of information reflected in their recommendations cannot offset the value of trend information.

Financial and Market Analysis of Post-Pandemic Tesla

Tesla is America’s biggest Electrical vehicle and technology firm, set up in 2003. Most famous for its Electric vehicle models and lithium battery technology, it has proven its success through its wide market share and grand market cap. The content of this paper includes, conducting a statistical analysis of Tesla during the past years, focusing on their successful market strategy practiced countering the COVID-19 pandemic, and in-depth analysis of past sales, price, and market share of each Tesla model then comparing the other competitors existing in the automotive industry, these results were used to determine the successfulness of Tesla’s strategy towards Economic shocks. At the end of this paper, SWOT analyses were used to provide advice for Tesla’s future business strategy. The purpose of this paper is to provide similar businesses with possible future market strategies by analyzing successful past strategies performed by Tesla Ltd and suggesting future goals Tesla could work towards.

SimVGNets: Similarity-Based Visibility Graph Networks for Carbon Price Forecasting

In response to global warming, carbon trading market emerges to reduce carbon emissions. However, uncertain fluctuations and complicated price mechanisms in the market have changed the characteristics of carbon data. Existing methods cannot track underlying structures among carbon data streams, failing to explain high variations and predict carbon prices accurately. In this case, how to predict carbon price is a problem demanding new and prompt solutions. Unlike existing predictive mechanisms with fixed structure, in this work, we investigate carbon price from a novel perspective of visibility graph networks, developing a variant forecasting framework based on visible relationships. Firstly, carbon prices are mapped into networks where data are transformed into corresponding nodes utilizing visibility algorithm. Then a novel similarity index is proposed to measure similarity distribution of nodes converted from carbon data, which extracts past and recent price information rather than only considering recent input, resulting in the loss of past knowledge. The forecasting model is finally established based on mutual visibility machamism and similarity of constructed carbon network. To verify the proposed method, experiments are conducted on real carbon datasets, and the results demonstrate the method is superior to existing models, suggesting the predictive potentials of graph network science. It is hoped that this work can act as a bridge between carbon data and networks, motivating further research on carbon price forecasting from graph network views.

Stock price prediction with long short-term memory

Stock forecasting aims to predict future stock prices based on past price changes in the market, playing an essential role in the field of financial transactions. However, since the stock market is highly uncertain, stock prediction is complex and challenging. This paper uses the long short-term memory (LSTM) model to predict the stock market and compares it with the current stock prediction algorithm. Firstly, we preprocessed the raw dataset and normalized data into the range from 0 to 1. Secondly, we introduced the LSTM model and improved its performance by tuning four parameters: learning rate, number of hidden layers, number of epochs, and batch size. Finally, we use four evaluation metrics to evaluate models: mean average error (MAE), root mean square error (RMSE), coefficient of determination (R2), and mean absolute error percentage (MAPE). Our LSTM model performs better than the previous model in experiments in terms of MAE, RMSE, R2, and MAPE.

An International Study of Application of Long Short-Term Memory (LSTM) Neural Networks for the prediction of stock and forex markets

This study focuses on the use of Long Short-Term Memory (LSTM) neural networks for stock and currency market forecasting. Accurate projections are difficult to make because of the complex dynamics and non-linear interactions that characterise financial markets. A Recurrent Neural Networks (RNN) variation called LSTM is particularly good at identifying temporal dependencies and long-term patterns in sequential data. The goal of LSTM models is to discover significant insights and produce precise predictions using past price data. In addition to discussing data pretreatment methods, model creation, and assessment metrics related to stock and FX market prediction, this work examines the benefits of LSTM in capturing market dynamics. Case studies and empirical analysis are used to investigate the capabilities and constraints of LSTM models in forecasting market movements.

Optimal portfolio with relative performance and partial information: A mean‐field game approach

AbstractWe use a mean‐field game (MFG) formulation to investigate a family of portfolio management problems with relative performance in a partially observable financial market. The stock price process is characterized by a factor model and investors can only observe past stock price data. All investors share a common investment period and have constant absolute risk aversion (CARA) utility functions. We construct explicit linear feedback equilibria for both a finite population game and a corresponding MFG. Filtering theory and dynamic programming method are involved to solve two kinds of optimization problems with partial information. Our results show that the limiting strategy for ‐agent game can be derived as equilibrium of suitable MFG in a partially observable financial market.

The effect of gold, dollar and Composite Stock Price Index on cryptocurrency

This paper aims to analyze cryptocurrency volatility by examining the effect of Gold, Dollar Index, and Composite Stock Price Index (IHSG) as independent variables and on Bitcoin and Ethereum as dependent variables. The cryptocurrency objects in this study are Bitcoin and Ethereum, which have the largest market capitalization. The data in this study used the period January 1, 2018, to December 31, 2021. This study used GARCH analysis. This study's results indicate that Bitcoin's volatility is influenced by the price of Bitcoin itself, gold, and the stock exchange index, and Ethereum and the stock exchange index influence Ethereum. This shows that the cryptocurrency market is inefficient as the prices are also affected by past prices.

Price Prediction of Cryptocurrency based on LSTM Model: Evidence from Ethereum

Contemporarily, under the impacts of COVID-19 and regional conflicts with radicalness fiscal policy, the prices of cryptocurrency have been fluctuated dramatically. Among various types of cryptocurrency, Ethereum is one of the most volatility assets. In order to avoid risks as well as gain extra return in the crypto market, it is necessary to construct accurate prediction approach. In this paper, the Long Short-Term Memory algorithm will be used to predict the future price of Ethereum by learning Ethereum's past price direction data. price trend by learning Ethereum's past price trend data. Based on the analysis, the predicted values of the trained model fit well with the actual data, with the regression evaluation index R2 of 97.08% and MAPE of 6.89%. According to the results, it is feasible to predict the future price trend through the past price trend data. Nevertheless, it should be noted that the stochastic process in data training might lead to the instability of model performances. Hence, it is necessary to train the data several time to select the best models. Overall, these results shed light on guiding further exploration of cryptocurrency price forecasting in terms of the state-of-art neural networks.

Detecting the Herding Behaviour in the South African Stock Market and its Implications

Herd mentality is associated with financial market bubbles and crises and is not a new concept. Despite the dotcom bubble in the late 90’s and the recent real estate bubble, investors still have the tendency to see irrational decision as rational. Thus the aim of this study was to investigate the presence of herding in the Johannesburg stock exchange during the Covid-19 pandemic. Studies conducted in other markets have detected the presence of herding which sends strong signal of issues that may arise in financial markets. Using a sample period from 02 January 2020 to 31 December 2021 and a cross sectional absolute deviation as an analysis tool, the findings reveals a significant presence of herding in the Johannesburg stock exchange. The main implications to this finding is that momentum investing strategies is more suitable for the Johannesburg Stock exchange and exploring past price movements may provide valuable insights on expected future price movements

Data Valuation Model for Estimating Collateral Loans in Corporate Financial Transaction

The importance of data assets as intangible corporate assets is being emphasized as more business activities based on digital technology are being carried out. This study proposes the development of a data valuation model that can enable companies to use data assets as collateral for loans in financial transactions. To this end, a model was designed with a focus on the cost approach, which is less likely to involve arbitrariness and error among other valuation model approaches. Furthermore, a model simulation was conducted after securing transaction data of a Korean secondhand marketplace provider. Among the total costs of this marketplace provider, the cost of using data reflecting the ratio of data activities was derived, focusing on financial statements and tangible and intangible assets for the last five years. The data asset acquisition costs were derived, and the data replacement costs were calculated by reflecting the past price and wage growth rates. The results revealed that simulation companies could use a total of KRW 26.8 billion worth of data as collateral for a loan. Accordingly, the data valuation model developed in this study will contribute to reinforcing the value of corporate data assets and proposing a new means of corporate financing.

Russian housing price forecast based on BP neural Network

Contemporarily, Russia's real estate market has huge potential for investors. The purpose of this paper is to investigated the impact of various factors on the real estate market price based on various statistical models. We collected past housing price data from the Internet. Subsequently, PCA model was adopted to reduce the dimension of the data to simplify the calculation. Finally, the BP model is implemented to predict the price changes in the real estate market. After calculation, our forecast data and the reality of housing prices are slightly different, but the forecast of the real estate market is consistent with the reality. In order to further find the influence of various factors on housing price, we also calculated the characteristic importance of different factors and found the most important influencing factor, i.e., the region. These results can provide investors with an idea to analyze the real estate market prices, and help policy makers find the real estate market prices in line with the market rules to avoid the occurrence of bubbles.

Understanding Momentum and Reversal Investing Strategies

Momentum and reversals are two phenomena to explain the past return trend. Originally introduced by Jegadeesh and Titman in 1993, momentum is now a common investment strategy when investors are trading securities. It points out the stock price may have a relationship with their past performance. A large number of researchers have been trying to find out the momentum investment effect based on empirical evidence in different markets in different investment periods, which include short term, medium term and long term. Moreover, a series of research concludes that the momentum investment strategy tends to help investors to get a higher return. In recent years, a large number of researchers have focused more on analysing financial markets in China, and they have paid more attention to improving the traditional Jegadeesh and Titman models. In addition, an increasing number of researchers also point out that noise trading is quite important in the security investment strategy, and the investors who are using a momentum investment strategy to trade their portfolios are supposed to take the noise trading strategy into consideration. Then a series of papers have been tried to explain the sources of the momentum effect, either risk-based or behavioural-based. When it assumes that the market is efficient, the past market prices could be reflected, and it is hard to get excess returns by observing the stock's past prices performance. Nevertheless, the momentum effect is likely to examine that the market is inefficient. When it tries to explain the momentum effect in risk-based sources, the abnormal price return may be derived from a risk that is undiversifiable. As for the behaviour-based explanation, several behavioural biases applied by different researchers could be used to study the momentum effect, such as cognitive errors, including the conservatism bias, the representative bias, and the emotional bias, including the loss aversion bias, the overconfidence bias and the self-attribution bias. Another possible explanation within this behavioural source is that overreaction to the news leads to the existence of price momentum.