Stock Trading Decision Research Articles

Emotional intelligence and behavioral biases on millennial stock trading decisions: a case study of Bibit investors

Emotional intelligence and behavioral biases on millennial stock trading decisions: a case study of Bibit investors

Social Media, Investment Knowledge, and Meme Stock Trading

This study investigates the influence of social media and investment knowledge on meme stock trading, using data from the 2021 National Financial Capability Study. It examines the relationships among social media usage, investor decisions, and the role of investment knowledge. The study emphasizes the significant impact of social media use on investor behavior within the meme stock phenomenon. No association was found between investment knowledge and meme stock trading decisions. However, the interaction of investment knowledge and the use of social media was significantly negative, illustrating that the influence of social media use on meme stock trading weakens when investors have greater investment knowledge.

Unveiling investors' substitution behavior: Stock trading decisions in response to housing market dynamics

Utilizing positive quasi-random shocks to local housing prices in Shanghai, we show that stock investors who experienced significant returns from the real estate market traded less actively, took less risk, and spent less effort trading. We confirm the effect of housing price changes on investors' trading behavior in a national sample. Our findings suggest a substitution effect between the real estate and stock market and highlight the importance of understanding investors' trading behavior in light of intertemporal variations in other asset market.

An intelligent stock trading decision system based on ensemble classifier through multimodal perturbation

Candlesticks are widely used as an effective technical analysis tool in financial markets. Traditionally, different combinations of candlesticks have formed specific bullish/bearish patterns providing investors with increased opportunities for profitable trades. However, most patterns derived from subjective expertise without quantitative analysis. In this article, combining bullish/bearish patterns with ensemble learning, we present an intelligent system for making stock trading decisions. The Ensemble Classifier through Multimodal Perturbation (ECMP) is designed to generate a diverse set of precise base classifiers to further determine the candlestick patterns. It achieves this by: first, introducing perturbations to the sample space through bootstrap sampling; second, employing an attribute reduction algorithm based on neighborhood rough set theory to select relevant features; third, perturbing the feature space through random subspace selection. Ultimately, the trading decisions are guided by the classification outcomes of this procedure. To evaluate the proposed model, we apply it to empirical investigations within the context of the Chinese stock market. The results obtained from our experiments clearly demonstrate the effectiveness of the approach.

Impact of Behavioral Biases on Investors’ Stock Trading Decisions: A Comprehensive Quantitative Analysis

Objective: This paper aims to investigate the impact of specified behavioral biases on investors' stock trading decisions in North India. It has been observed that most of the research works are based on financial theories, which affect investment decisions. But besides the theories nowadays, behavioral biases also play an important role in investment decisions, which was less focused in the previous literature. Methods: The study used primary data collected from a sample from North Indian States (Uttar Pradesh, Delhi, Haryana, and Punjab) through a structured questionnaire to analyze the impact of specified behavioral biases on investors' stock trading decisions. We used structural equation modelling to find out the significant impact of behavioral biases on stock trading and investment decisions. Findings: The investigation determined that the majority of the designated cognitive biases, such as the Overconfidence Bias, the Representativeness Bias, and the Herding Bias, exert a significant influence on the decisions about stock trading and investment made by investors. Novelty: The ample research in this domain has primarily occurred in various countries, with only a limited number of studies conducted specifically at the Indian level. Nevertheless, based on the literature review, it is evident that this study is groundbreaking in North India. The objective of this research is to enhance the effectiveness of financial advisors by gaining a deeper understanding of the psychological aspects of clients. This, in turn, will aid in developing portfolios tailored to individual behavior, aligning with client preferences. Recognizing and addressing behavioral biases is crucial for individual investors as they strive to make informed and successful financial decisions. Keywords: Behavioral Biases, Overconfidence (OC) bias, Representativeness Bias (RB), Herding Bias (HB), Structural Equation Modelling

An effective approach for predicting daily stock trading decisions using fuzzy inference systems

An effective approach for predicting daily stock trading decisions using fuzzy inference systems

Emotional intelligence and stock trading decisions: Indonesia’s millennial generation in the COVID-19 era

Emotional intelligence and stock trading decisions: Indonesia’s millennial generation in the COVID-19 era

A Deep Learning Approach with Extensive Sentiment Analysis for Quantitative Investment

Recently, deep-learning-based quantitative investment is playing an increasingly important role in the field of finance. However, due to the complexity of the stock market, establishing effective quantitative investment methods is facing challenges from various aspects because of the complexity of the stock market. Existing research has inadequately utilized stock news information, overlooking significant details within news content. By constructing a deep hybrid model for comprehensive analysis of historical trading data and news information, complemented by momentum trading strategies, this paper introduces a novel quantitative investment approach. For the first time, we fully consider two dimensions of news, including headlines and contents, and further explore their combined impact on modeling stock price. Our approach initially employs fundamental analysis to screen valuable stocks. Subsequently, we built technical factors based on historical trading data. We then integrated news headlines and content summarized through language models to extract semantic information and representations. Lastly, we constructed a deep neural model to capture global features by combining technical factors with semantic representations, enabling stock prediction and trading decisions. Empirical results conducted on over 4000 stocks from the Chinese stock market demonstrated that incorporating news content enriched semantic information and enhanced objectivity in sentiment analysis. Our proposed method achieved an annualized return rate of 32.06% with a maximum drawdown rate of 5.14%. It significantly outperformed the CSI 300 index, indicating its applicability to guiding investors in making more effective investment strategies and realizing considerable returns.

Ameliorated graph sample and aggregate network and convolutional neural network for stock trading decisions

Both graph neural network and convolutional neural network (CNN) perform well in predicting the trading signals of time series data. However, these models often encounter challenges in profitability due to either inadequate information or algorithmic limitations. A model AG-MCNN is proposed by combining ameliorated graph sample and aggregate network (GraphSAGE) and multi-CNN for stock trading decisions. Ameliorated GraphSAGE (AG) is used to learn graph embedding in daily market network constructed from comprehensive market information, with neighbors having higher correlation exerting stronger influence on the target node. Moreover, a more reasonable graph modeling approach is adopted to preserve the important features of time series while filtering out noise. Multi-scale time series and comments with sentiment information are collected to analyze short-term and long-term trends. The short-term features of the weighted graph are extracted by a dual concatenated CNN. The pre-trained model is fine-tuned based on transfer learning to address the issue of insufficient sentiment information in the long-term trend. Experimental results demonstrate that AG-MCNN achieves an average profit rate of 68.4094%, compared to the highest value of 46.1091% for comparison models and 18.2234% for the buy-and-hold strategy. AG-MCNN’s high level of profitability in stock investments makes it a promising choice for investors.

DEEP REINFORCEMENT LEARNING ON STOCK DATA

This study proposes using Deep Reinforcement Learning (DRL) for stock trading decisions and prediction. DRL is a machine learning technique that enables agents to learn optimal strategies by interacting with their environment. The proposed model surpasses traditional models and can make informed trading decisions in real-time. The study highlights the feasibility of applying DRL in financial markets and its advantages in strategic decision- making. The model's ability to learn from market dynamics makes it a promising approach for stock market forecasting. Overall, this paper provides valuable insights into the use of DRL for stock trading decisions and prediction, establishing a strong case for its adoption in financial markets. Keywords: reinforcement learning, stock market, deep reinforcement learning.

Improving stock trading decisions based on pattern recognition using machine learning technology.

PRML, a novel candlestick pattern recognition model using machine learning methods, is proposed to improve stock trading decisions. Four popular machine learning methods and 11 different features types are applied to all possible combinations of daily patterns to start the pattern recognition schedule. Different time windows from one to ten days are used to detect the prediction effect at different periods. An investment strategy is constructed according to the identified candlestick patterns and suitable time window. We deploy PRML for the forecast of all Chinese market stocks from Jan 1, 2000 until Oct 30, 2020. Among them, the data from Jan 1, 2000 to Dec 31, 2014 is used as the training data set, and the data set from Jan 1, 2015 to Oct 30, 2020 is used to verify the forecasting effect. Empirical results show that the two-day candlestick patterns after filtering have the best prediction effect when forecasting one day ahead; these patterns obtain an average annual return, an annual Sharpe ratio, and an information ratio as high as 36.73%, 0.81, and 2.37, respectively. After screening, three-day candlestick patterns also present a beneficial effect when forecasting one day ahead in that these patterns show stable characteristics. Two other popular machine learning methods, multilayer perceptron network and long short-term memory neural networks, are applied to the pattern recognition framework to evaluate the dependency of the prediction model. A transaction cost of 0.2% is considered on the two-day patterns predicting one day ahead, thus confirming the profitability. Empirical results show that applying different machine learning methods to two-day and three-day patterns for one-day-ahead forecasts can be profitable.

SeMLP: Self-evolving Multi-layer Perceptron in Stock Trading Decision Making

There is a growing interest in automatic crafting of neural network architectures as opposed to expert tuning to find the best architecture. On the other hand, the problem of stock trading is considered one of the most dynamic systems that heavily depends on complex trends of the individual company. This paper proposes a novel self-evolving neural network system called self-evolving Multi-Layer Perceptron (seMLP) which can abstract the data and produce an optimum neural network architecture without expert tuning. seMLP incorporates the human cognitive ability of concept abstraction into the architecture of the neural network. Genetic algorithm (GA) is used to determine the best neural network architecture that is capable of knowledge abstraction of the data. After determining the architecture of the neural network with the minimum width, seMLP prunes the network to remove the redundant neurons in the network, thus decreasing the density of the network and achieving conciseness. seMLP is evaluated on three stock market data sets. The optimized models obtained from seMLP are compared and benchmarked against state-of-the-art methods. The results show that seMLP can automatically choose best performing models.

Procena optimalnih ekonomskih i tehničkih indikatora za donošenje odluke o trgovanju poljoprivrednim akcijama

The main goal of the research was to determine which indicators are the most impactful on the buy and sell triggers of stocks to maximize profits of the trade. The aim was to determine the agriculture stock price movements based on economic and technical indicators. The investors in the stock market want to maximize trade profits by buying or selling the stocks. Technical and economic analyses are conducted to determine whether to sell or buy agriculture stocks. Since many factors could impact stocks profit decisions, it is essential to determine which parameter has more or less influence on the decision. For such a purpose adaptive neuro-fuzzy inference system (ANFIS) was used since the method is suitable for redundant and nonlinear data. Generally, technical indicators are more valuable and impactful for agricultural stock trading decision-making. Technical indicator moving average convergence and divergence (MACD) strongly influences the stock trading decision. Economic indicator relative change after smoothing 15 days federal rate has the most decisive influence on the stock trading decision.

Deep Reinforcement Learning & Feature Extraction For Constructing Alpha Generating Equity Portfolios

The ambition of this paper is to catch hidden information inside the Securities and Exchange Commission’s (SEC)13F public holding data in order to construct an equity portfolio that maximizes returns. The 13F ling data give us the quarterly stock trading decisions of included funds, but we’re not given any insight on how they made their decisions or if information has been shared between funds. To remedy this lack of knowledge, this paper used feature extraction in order to lter out the best performing funds through several criteria. We propose a method employing powerful machine learning techniques (Deep Reinforcement Learning) to try to catch the missing pieces of information behind the decision process and use them as a prediction tool to construct quarterly equity portfolios. This approach reached an annualized return of 21% with a sharpe ratio of 1.8 outperforming the S&P 500 both in returns and stability through historical backtesting.

Hybrid Model Approach to the Complexity of Stock Trading Decisions in Turkey

The aim of this paper is to predict the Borsa Istanbul (BIST) 30 index movements to determine the most accurate buy and sell decisions using the methods of Artificial Neural Networks (ANN) and Genetic Algorithm (GA). We combined these two methods to obtain a hybrid intelligence method, which we apply. In the financial markets, over 100 technical indicators can be used. However, several of them are preferred by analysts. In this study, we employed nine of these technical indicators. They are moving average convergence divergence (MACD), relative strength index (RSI), commodity channel index (CCI), momentum, directional movement index (DMI), stochastic oscillator, on-balance volume (OBV), average directional movement index (ADX), and simple moving averages (3-day moving average, 5-day moving average, 10-day moving average, 14-day moving average, 20-day moving average, 22-day moving average, 50-day moving average, 100-day moving average, 200-day moving average). In this regard, we combined these two techniques and obtained a hybrid intelligence method. By applying this hybrid model to each of these indicators, we forecast the movements of the Borsa Istanbul (BIST) 30 index. The experimental result indicates that our best proposed hybrid model has a successful forecast rate of 75%, which is higher than the single ANN or GA forecasting models.

An intelligent stock trading decision support system based on rough cognitive reasoning

From the perspective of Momentum Investing (MI), more profitable trading opportunities for bullish investors would exist in the stocks occurring with limit-up. Motivated by this, we propose an intelligent stock trading decision support system by using rough cognitive reasoning, based on which stocks with the higher probabilities of rising in the short term after the occurrences of limit-up can be distinguished. Considering financial markets are full of uncertainty and high noise, an extended rough cognitive network (RCN) is established, which is a granular reasoning model based on rough set theory and fuzzy cognitive maps. As a kind of reasoning mechanism, the extended RCN can effectively analyze both the continuous and discrete features of financial data to deal with the uncertainty and inconsistency. Moreover, entropy-based method is involved into the extended RCN model such that the knowledge representation of model can be further improved, and harmony search algorithm is applied for optimization. The proposed model is further applied in Chinese stock market to carry out empirical studies, where the discussion on parameters are implemented and experiment results show the effectiveness and validity of the proposed model.

Is Senator Burr Guilty of Insider Trading under the STOCK Act?

The SEC and U.S. Justice Department are investigating suspicious stock trades made by U.S. Senator Richard Burr in February 2020, which might have been based in part upon confidential information about the Coronavirus pandemic disclosed at closed-door congressional hearings, which if true, might violate the Stop Trading on Congressional Knowledge, or STOCK Act. The investigations may turn on whether Senator Burr’s trades were based upon confidential government information, or publicly available news reports.The outcome of the investigation into Senator Burr’s trading may depend on the nature and timing of the confidential briefings and public disclosures about COVID-19, as compared to the nature and timing of Senator Burr’s trades. Evidence that Senator Burr relied upon both public and non-public information to make his stock trading decisions could muddy the waters of a potential criminal proceeding, with its attendant legal standard of proof beyond a reasonable doubt. But the Securities and Exchange Commission would encounter only a civil burden of proof, which could be more readily proven. And, in any event, Senator Burr faces a Senate ethics investigation, and bipartisan criticism for the optics of publicly urging calm while privately dumping large quantities of his stock portfolio.

Application of deep reinforcement learning in stock trading strategies and stock forecasting

The role of the stock market across the overall financial market is indispensable. The way to acquire practical trading signals in the transaction process to maximize the benefits is a problem that has been studied for a long time. This paper put forward a theory of deep reinforcement learning in the stock trading decisions and stock price prediction, the reliability and availability of the model are proved by experimental data, and the model is compared with the traditional model to prove its advantages. From the point of view of stock market forecasting and intelligent decision-making mechanism, this paper proves the feasibility of deep reinforcement learning in financial markets and the credibility and advantages of strategic decision-making.

Short Term Firm-Specific Stock Forecasting with BDI Framework

In today’s information age, a comprehensive stock trading decision support system which aids a stock investor in decision making without relying on random guesses and reading financial news from various sources is the need of the hour. This paper investigates the predictive power of technical, sentiment and stock market analysis coupled with various machine learning and classification tools in predicting stock trends over the short term for a specific company. Large dataset stretching over a duration of ten years has been used to train, test and validate our system. The efficacy of supervised non-shallow and prototyping learning architectures are illustrated by comparison of results obtained through myriad optimization, classification and clustering algorithms. The results obtained from our system reveals a significant improvement over the efficient market hypothesis for specific companies and thus strongly challenges it. Technical parameters and algorithms used have shown a significant impact on the predictive power of the system. The predictive accuracy obtained is as high as 70–75% using linear vector quantization. It has been found that sentiment analysis has strong correlation with the future market trends. The proposed system provides a comprehensive decision support system which aids in decision making for stock trading. We also present a novel application of the BDI framework to systematically apply the learning and prediction phases.

Stock trading decisions using ensemble-based forecasting models: a study of the Indian stock market

In this paper, a two-phase ensemble framework comprising of various non-classical decomposition models, namely, Empirical Mode Decomposition, Ensemble Empirical Mode Decomposition and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and machine learning models, namely, Artificial Neural Network and Support Vector Regression (SVR), is proposed for predicting the stock prices. In the first phase, historical stock prices are decomposed to a set of subseries. In the second phase, each subseries is forecasted using machine learning algorithms. Lastly, forecasts of individual subseries are added to obtain the final forecasts. The proposed framework is tested on constituents of Nifty index for a period of 8 years ranging from 2008 to 2015. Performance of the models were analysed using root mean square error. Further, the results were validated statistically using Wilcoxon Signed Rank Test and Friedman Test. CEEMDAN-SVR model outperformed the remaining models. In addition, trading rules were illustrated to determine the optimal timing for buying/selling the stocks. Trading rules based on ensemble models yielded higher return on investment compared to traditional Buy-and-Hold strategy.