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Time and Frequency Analysis Using the ARMA Model: Evidence from the Indian Stock Market
Published in Avinash K. Shrivastava, Rana Sudhir, Amiya Kumar Mohapatra, Mangey Ram, Advances in Management Research, 2019
Practitioners and academicians have proposed various models based on modern and traditional theories of investments such as random walk theory, the capital asset pricing model, and technical and fundamental analyses to give a more accurate forecast. The fundamental analysis entails an in-depth analysis of the economy, industry, and company, assuming that the trading price of the company depends on its intrinsic value and the expected return of the investors. In contrast, the technical analysis assesses past stock price trends and uses price, volume, and open interest statistical charts to predict future prices with correctional changes. The philosophy behind the technical analysis is that any factor can affect the market at any given point of time. It may be internal or external (Mendelsohn, 2000).
Selective transfer learning with adversarial training for stock movement prediction
Published in Connection Science, 2022
Yang Li, Hong-Ning Dai, Zibin Zheng
In the field of stock movement prediction, fundamental analysis and technical analysis are the two basic methods. Fundamental analysis attempts to measure the intrinsic value of a stock by examining related economic, financial and other qualitative and quantitative factors (Bollen et al., 2011; Ding et al., 2015; Xu & Cohen, 2018). Technical analysis tends to take the historical market data to predict its future movement with advanced models in recent years (Feng, Chen et al., 2019; Li et al., 2019; Qin et al., 2017). Our work falls into the technical analysis.
Intraday trend prediction of stock indices with machine learning approaches
Published in The Engineering Economist, 2023
With the development of artificial intelligence, methods of machine learning are gradually being implemented in the field of financial research. Due to the availability and reliability of stock data, stock trend prediction is a prominent research topic. From the perspective of data dimensions, stock prediction can be used to test whether machine learning can be applied to massive stock data. And from the perspective of data structure, it is worthwhile to investigate whether machine learning can acquire relevant feature information from nonlinear and sparse stock data. From an application standpoint, it is worthwhile to investigate if trading strategies can be constructed using high-frequency stock data combined with machine learning techniques. Therefore, stock market forecasting has become a major topic of study recently, and researchers have published a substantial amount of literature in this area (Jiang, 2021). In the early days, before the widespread usage of artificial intelligence tools, stock trend prediction was primarily divided into fundamental and technical analyses (Edwards et al., 2018). The fundamental analysis method assesses a stock’s investment and intrinsic value by analyzing fundamental company data (such as revenue and earnings) and macroeconomic data. The intrinsic value of the stock is compared with the market price of the stock, and recommendations for buying and selling the stock are formed. Technical analysis is a method of predicting the direction of market price changes through the analysis of market behavior itself. Mainly, the daily trading status of the stock market, including information on price changes and changes in trading volume and position, is drawn into graphs or charts in chronological order. Hence, support and resistance, momentum, relative price strength, and other indicators based on price and volume can be further calculated. Stock price and volume charts, as well as multiple technical indicators, are used to predict stock price movements. Fundamental analysis focuses on analyzing the primary factors affecting the price, while technical analysis focuses on analyzing the price change pattern. Fundamental analysis is based on the future, using economic development forecasts to analyze price movements.