Single step estimation of ARMA roots for non-fundamental nonstationary fractional models

We propose a single step estimator for the autoregressive and moving-average roots (without imposing causality or invertibility restrictions) of a nonstationary Fractional ARMA process. These estimators employ an efficient tapering procedure, which allows for a long memory component in the process, but avoid estimating the nonstationarity component, which can be stochastic and/or deterministic. After selecting automatically the order of the model, we robustly estimate the AR and MA roots for trading volume for the thirty stocks in the Dow Jones Industrial Average Index in the last decade. Two empirical results are found. First, there is strong evidence that stock market trading volume exhibits non-fundamentalness. Second, non-causality is more common than non-invertibility.

E xploration E xploitation Problem in Policy Based Deep Reinforcement Learning for Episodic and Continuous Environments

Reinforcement learning is an artificial intelligence paradigm that enables intelligent agents to accrue environmental incentives to get superior results. It is concerned with sequential decision-making problems which offer limited feedback. Reinforcement learning has roots in cybernetics and research in statistics, psychology, neurology, and computer science. It has piqued the interest of the machine learning and artificial intelligence groups in the last five to ten years. It promises that it allows you to train agents using rewards and penalties without explaining how the task will be completed. The RL issue may be described as an agent that must make decisions in a given environment to maximize a specified concept of cumulative rewards. The learner is not taught which actions to perform but must experiment to determine which acts provide the greatest reward. Thus, the learner has to actively choose between exploring its environment or exploiting it based on its knowledge. The exploration-exploitation paradox is one of the most common issues encountered while dealing with Reinforcement Learning algorithms. Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. We describe how to utilize several deep reinforcement learning (RL) algorithms for managing a Cartpole system used to represent episodic environments and Stock Market Trading, which is used to describe continuous environments in this study. We explain and demonstrate the effects of different RL ideas such as Deep Q Networks (DQN), Double DQN, and Dueling DQN on learning performance. We also look at the fundamental distinctions between episodic and continuous activities and how the exploration-exploitation issue is addressed in their context.

Application of multilayer perceptron to deep reinforcement learning for stock market trading and analysis

Trading strategies to maximize profits by tracking and responding to dynamic stock market variations is a complex task. This paper proposes to use a multilayer perceptron method (a part of artificial neural networks (ANNs)), that can be used to deploy deep reinforcement strategies to learn the process of predicting and analyzing the stock market products with the aim to maximize profit making. We trained a deep reinforcement agent using the four algorithms: proximal policy optimization (PPO), deep Q-learning (DQN), deep deterministic policy gradient (DDPG) method, and advantage actor critic (A2C). The proposed system, comprising these algorithms, is tested using real time stock data of two products: Dow Jones (DJIA-index), and Qualcomm (shares). The performance of the agent linked to the individual algorithms was evaluated, compared and analyzed using Sharpe ratio, Sortino ratio, Skew and Kurtosis, thus leading to the most effective algorithm being chosen. Based on the parameter values, the algorithm that maximizes profit making for the respective financial product was determined. We also extended the same approach to study and ascertain the predictive performance of the algorithms on trading under highly volatile scenario, such as the pandemic coronavirus disease 2019 (COVID-19).

The stock market as a casino: Associations between stock market trading frequency and problem gambling

Background and aims Personal investors decrease their stock market investment returns by trading frequently, which the behavioral finance literature has primarily explained via investors' overconfidence and low levels of financial literacy. This study investigates whether problem gambling can help account for frequent trading in a sample of active gambler/investors, as suggestive of frequent trading being in part driven by a behavioral addiction to gambling-like activities. Methods A retrospective cross-sectional study of 795 US-based participants, who reported both being active gamblers and holding stock market investments. Recollected stock trading activity (typical portfolio size, purchases and sales of stocks) was compared with scores on the Problem Gambling Severity Index, a financial literacy scale, and a measure of overconfidence. Results Self-reported relative stock portfolio turnover was positively associated with problem gambling scores. This association was robust to controls for financial literacy, overconfidence, and demographics, and occurred equally among investors of all self-reported portfolio sizes. Discussion and conclusions This study provides support for the hypothesis that behavioral addiction to gambling-like activities is associated with frequent stock market trading. New investment products that increase the ease of trading may therefore be detrimental to some investors.

STOCK MARKET PREDICTION: USING ECONOMETRIC MODELS AND NEURAL NETWORKS

Stock market trading involves buying and selling of shares or stocks, which represents ownership of business. This research paper will focus on capturing the algorithmic trading based on historical data and compare present day algorithms to nd the best t model to understand the underlying patterns in stock market trading. A comparative analysis of closing stock price for 12 companies from three different sectors has been considered to understand the efcacy of the models in order to predict the future stock prices with minimal errors. Stock market was earlier predicted using traditional econometric models like the ARIMA and SARIMA, however, in this paper, Machine Learning, a part of Articial Intelligence will be incorporated in the stock data collected from Yahoo Finance to train models and provide predictions/decisions without being explicitly programmed to do so. Models such as OLS, SARIMA, Convolutional Neural Networks and Recursive Neural Networks (LSTM) will also be used to analyze the historical stock data and will be compared for accuracy using testing parameters like Mean Squared Error (MSE).

The stock market as a casino: Associations between costly excessive stock market trading and problem gambling

The stock market should be a unique kind of casino, where the average person wins money over time. However, previous research shows that excessive stock market trading can contribute to financial losses --- just like in any other casino. While gambling research has documented the adverse consequences of problem gambling, there has been comparatively less behavioral finance research on the correlates of excessive stock market trading. This study aimed to document whether excessive stock trading was positively associated with problem gambling, and whether this hypothesized association was robust to controlling for demographics, and objective measures of overconfidence and financial literacy in a convenience sample of 798 US investors. We found that self-reported relative stock portfolio turnover was positively associated with problem gambling, that this association was robust to controls, and occurred equally over investors of all self-reported portfolio sizes. This study showed that problem gamblers may also make suboptimal risky choices more generally, and that a behavioral dependence explanation for suboptimal investment decisions should be subject to further investigation in the behavioral finance literature.