Published14 Apr Abstract According to the forecast of stock price trends, investors trade stocks. In recent years, many researchers focus on adopting machine learning ML algorithms to predict stock price trends.
However, their studies were carried out on small stock datasets with limited features, short backtesting period, and no consideration of transaction cost.
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And their experimental results lack statistical significance test. In this paper, on large-scale trading learning algorithm datasets, we synthetically evaluate various ML algorithms and observe the daily trading performance of stocks under transaction cost and no transaction cost. The experimental results demonstrate that traditional ML algorithms have a better performance in most of the directional evaluation indicators.
Unexpectedly, the performance of some traditional ML algorithms is not much worse than that of the best DNN models without considering the transaction cost. Moreover, the trading performance of all ML algorithms is sensitive to the changes of transaction cost.
Compared with the traditional ML algorithms, DNN models have better performance considering transaction cost.
Meanwhile, the impact of transparent transaction cost and implicit transaction cost on trading performance are different. Our conclusions are significant to choose the best algorithm for stock trading in different markets.
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Introduction The stock market plays a very important role in modern economic and social life. Investors want to maintain or increase the value of their assets by investing in the stock of the listed trading learning algorithm with higher expected earnings.
As a listed company, issuing stocks is an important tool to raise funds from the public and expand the scale of the industry. In modern financial market, successful investors are good at making use of high-quality information to make investment decisions, and, more importantly, they can make quick and effective decisions based on the information they have already had.
Therefore, the field of stock investment attracts the attention not only of financial practitioner and ordinary investors but also of researchers in academic [ 1 ]. In the past many years, researchers mainly constructed statistical models to describe the time series of stock price and trading volume to forecast the trends of future stock returns [ 2 — 4 ].
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It is worth noting that the intelligent computing methods represented by ML algorithms also present a trading learning algorithm development momentum in stock market prediction with the development of artificial trading learning algorithm technology. The main reasons are as follows. From the early linear model, support vector machine, and shallow neural network to DNN models and reinforcement learning algorithms, intelligent computing methods have made significant improvement.
They have been effectively applied to the fields of image recognition and text analysis. In some papers, the authors think that these advanced algorithms can capture the dynamic changes of the financial market, simulate the trading process of stock, and make automatic investment decisions. High-performance computer equipment, accurate and fast intelligent algorithms, and financial big data together can provide decision-making support for programmed and automated trading of stocks, which has gradually been accepted by industry practitioners.
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Therefore, the power of financial technology is reshaping the financial market and changing the format of finance. Over the years, traditional ML methods have shown strong ability in trend prediction of stock prices [ 2 — 16 ].
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In recent years, artificial intelligence computing methods represented by DNN have made a series of major breakthroughs in the fields of Natural Language Processing, image classification, voice translation, and so on. It is noteworthy that some DNN algorithms have been applied for time series prediction and quantitative trading [ 17 — 34 ].
However, most of the binomial options models studies focused on the prediction of the stock index of major economies in the world [ 28111315 — 1722293032 ], etc. Meanwhile, there is no statistical significance test between different algorithms which were used in stock trading [ 8 — 1132 ], etc.
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That is, the comparison and evaluation of the various trading algorithms lack large-scale stocks datasets, considering transaction cost and statistical significance test.
Therefore, the performance of backtesting may tend to be overly optimistic. In this regard, we need to clarify two concerns based on a large-scale stock dataset: 1 whether the trading strategies based on the DNN models can achieve statistically significant results compared with the traditional ML algorithms without transaction cost; 2 how do transaction costs affect trading performance of the ML algorithm?
These problems constitute the main motivation of this research and they are very important for quantitative investment practitioners and portfolio managers. These solutions of these problems are of great value for practitioners to do stock trading. The label on the -th trading day is the symbol for the yield of the -th trading day relative to the -th trading day.
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That is, if the yield is positive, the label value is set to trading learning algorithm, otherwise 0. For each stock, we choose 44 technical indicators of trading days before December 31,to build a stock dataset. After the dataset of a stock is built, we choose the walk-forward analysis WFA method to train the ML models step by step.
Through multiple comparative analysis of the different transaction cost structures, the performance of trading algorithms is significantly smaller than that without transaction cost, which shows that trading performance is sensitive to transaction cost.