Selection of the optimal trading model for stock investment in different industries

Abstract

In general, the stock prices of the same industry have a similar trend, but those of different industries do not. When investing in stocks of different industries, one should select the optimal model from lots of trading models for each industry because any model may not be suitable for capturing the stock trends of all industries. However, the study has not been carried out at present. In this paper, firstly we select 424 S&P 500 index component stocks (SPICS) and 185 CSI 300 index component stocks (CSICS) as the research objects from 2010 to 2017, divide them into 9 industries such as finance and energy respectively. Secondly, we apply 12 widely used machine learning algorithms to generate stock trading signals in different industries and execute the back-testing based on the trading signals. Thirdly, we use a non-parametric statistical test to evaluate whether there are significant differences among the trading performance evaluation indicators (PEI) of different models in the same industry. Finally, we propose a series of rules to select the optimal models for stock investment of every industry. The analytical results on SPICS and CSICS show that we can find the optimal trading models for each industry based on the statistical tests and the rules. Most importantly, the PEI of the best algorithms can be significantly better than that of the benchmark index and “Buy and Hold” strategy. Therefore, the algorithms can be used for making profits from industry stock trading.

An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy

“…Meanwhile, we obtain SPICS and CSICS from Yahoo finance and Netease Finance, respectively. Secondly, the task of data preparation includes ex-dividend/rights for the acquired data, generating a large number of well-recognized technical indicators as features, and using max-min normalization to deal with the features, so that the preprocessed data can be used as the input of ML algorithms [34]. Thirdly, the trading signals of stocks are generated by the ML algorithms.…”

“…Given a training dataset D, the task of ML algorithm is to classify class labels correctly. In this paper, we will use six traditional ML models (LR, SVM, CART, RF, BN, and XGB) and six DNN models (MLP, DBN, SAE, RNN, LSTM, and GRU) as classifiers to predict the ups and downs of the stock prices [34]. The main model parameters and training parameters of these ML learning algorithms are shown in Tables 1 and 2. In Tables 1 and 2, features and class labels are set according to the input format of various ML algorithms in R language.…”

“…Performance evaluation indicator is used for evaluating the profitability and risk control ability of trading algorithms. In this paper, we use trading signals generated by ML algorithms to conduct the backtesting and apply the WR, ARR, ASR, and MDD to do the trading performance evaluation [34]. WR is a measure of the accuracy of trading signals; ARR is a theoretical rate of return of a trading strategy; ASR is a risk-adjusted return which represents return from taking a unit risk [37] and the risk-free return or benchmark is set to 0 in this paper; MDD is the largest decline in the price or value of the investment period, which is an important risk assessment indicator.…”

“…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][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. However, most of the previous studies focused on the prediction of the stock index of major economies in the world ([2, 8, 11, 13, 15-17, 22, 29, 30, 32], etc.)…”

A cooperative deep learning model for stock market prediction using deep autoencoder and sentiment analysis

“…Some studies use sentiment analysis methods, stock price datasets, and some works integrating stock price datasets and sentiment analysis are discussed in the literature review in Section 2. With the rapid developments in soft computing and different computational techniques in the last decades, researchers have proposed many methods based on artificial neural networks, heuristics, and meta-heuristics for stock market prediction ( Araújo, 2010 ; Vui et al, 2013 ; Shi & Zhuang, 2019 ; Lv et al, 2019 ; Atsalakis & Valavanis, 2009 ; Haider Bangyal et al, 2022 ; Pervaiz et al, 2021 ). News has a significant impact on stock prices.…”