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Forecasting Stock Index Return with Investor's Attention: A Research Based on Difference Evolution Extreme Learning Machine

TANG Min1,2, HUANG Zhigang1,2   

  1. 1. School of Economics and Management, Fuzhou University, Fuzhou 350108;
    2. Fujian Fintech Innovation Major Laboratory, Fuzhou 350108
  • Received:2021-04-09 Revised:2021-10-05 Online:2022-06-25 Published:2022-07-29

TANG Min, HUANG Zhigang. Forecasting Stock Index Return with Investor's Attention: A Research Based on Difference Evolution Extreme Learning Machine[J]. Journal of Systems Science and Mathematical Sciences, 2022, 42(6): 1503-1518.

Financial time series forecasting models based on traditional statistic theories have been applied to forecast stock indexes with the search volume. However, these models cannot identify the nonlinear relationship in the real stock market. This paper constructs an extreme learning machine (ELM) optimized with the differential evolutionary algorithm (DE) to examine the Baidu Indexes' forecasting ability. Three market-related Baidu Indexes are selected to predict four different Chinese stock indexes' returns. Empirical results suggest that DE-ELM model has a better prediction ability. Furthermore, Baidu indexes can improve the prediction accuracy of DE-ELM model, and the DE-ELM model extended with the Baidu index triplet has the highest forecasting accuracy.

CLC Number: 

[1] Lemieux J, Peterson R A.Purchase deadline as a moderator of the effects of price uncertainty on search duration.Journal of Economic Psychology, 2011, 32(1):33-44.
[2] Abbas A E, Bakir N O, Klutke G, et al.Effects of risk aversion on the value of information in two-action decision problems.Decision Analysis, 2013, 10(3):257-275.
[3] Da Z, Engelberg J, Gao P.In search of attention.The Journal of Finance, 2011, 66(5):1461-1499.
[4] Bordino I, Battiston S, Caldarelli G, et al.Web search queries can predict stock market volumes.Plos One, 2012, 7(7):1-46.
[5] Dimpfl T, Jank S.Can internet search queries help to predict stock market volatility?.European Financial Management, 2016, 22(2):171-192.
[6] 俞庆进,张兵.投资者有限关注与股票收益——以百度指数作为关注度的一项实证研究.金融研究, 2012,(8):152-165.(Yu Q J, Zhang B.Limited attention and stock performance:An empirical study using Baidu Index as the proxy for investor attention.Journal of Financial Research, 2012,(8):152-165.)
[7] 陈晓红,彭宛露,田美玉.基于投资者情绪的股票价格及成交量预测研究.系统科学与数学, 2016, 36(12):2294-2306.(Chen X H, Peng W L, Tian M Y.Stock market prediction based on investor sentiment.Journal of Systems Science and Mathematical Sciences, 2016, 36(12):2294-2306.)
[8] 陈植元,米雁翔,厉洋军,等.基于百度指数的投资者关注度与股票市场表现的实证分析.统计与决策, 2016,(23):155-157.(Chen Z Y, Mi Y X, Li Y J, et al.Empirical analysis of investor attention and stock market performance based on Baidu Index.Statistics&Decision, 2016,(23):155-157.)
[9] Preis T, Moat H S, Stanley H E.Quantifying trading behavior in financial markets using Google Trends.Scientific Reports, 2013, 3(1684):1-6.
[10] Curme C, Preis T, Stanley H E, et al.Quantifying the semantics of search behavior before stock market moves.Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(32):11600-11605.
[11] Laurens B, Glenn K, Peter M, et al.Google searches and stock returns.International Review of Financial Analysis, 2016, 45:150-156.
[12] Salisu A A, Ogbonna A E, Adediran I.Stock-induced Google Trends and the predictability of sectoral stock returns.Journal of Forecasting, 2021, 40(2):327-345.
[13] Wen F H, Xu L, Ouyang G, et al.Retail investor attention and stock price crash risk:Evidence from China.International Review of Financial Analysis, 2019, 65(101376):1-15.
[14] 赵龙凯,陆子昱,王致远.众里寻"股"千百度——股票收益率与百度搜索量关系的实证探究.金融研究, 2013,(4):183-195.(Zhao L K, Lu Z Y, Wang Z Y.The empirical study on relationship between stock returns and search volume from Baidu.Journal of Financial Research, 2013,(4):183-195.)
[15] Dong J C, Dai W, Li J J.Exploring the linear and nonlinear causality between internet big data and stock markets.Journal of Systems Science and Complexity, 2020, 33(3):783-798.
[16] Nadia V.Investor attention, index performance, and return predictability.Journal of Banking and Finance, 2014, 41:17-35.
[17] Li X, Ma J, Wang S, et al.How does Google search affect trader positions and crude oil prices?.Economic Modelling, 2015, 49:162-171.
[18] Jianchun F, Giray G, Chi-Keung M L, et al.The impact of Baidu Index sentiment on the volatility of China's stock markets.Finance Research Letters, 2020, 32(101099):1-8.
[19] 瞿慧,沈微.基于LSTHAR模型的投资者关注对股市波动影响研究.中国管理科学, 2020, 28(7):23-34.(Qu H, Shen W.The impact of investor attention on market volatility based on the LSTHAR model.Chinese Journal of Management Science, 2020, 28(7):23-34.)
[20] 张同辉,苑莹,曾文.投资者关注能提高市场波动率预测精度吗?——基于中国股票市场高频数据的实证研究.中国管理科学, 2020, 28(11):192-205.(Zhang T H, Yuan Y, Zeng W.Can investor attention help to predict stock market volatility?An empirical research based on Chinese stock market high-frequency data.Chinese Journal of Management Science, 2020, 28(11):192-205.)
[21] Wang J Z, Wang J J, Zhang Z G, et al.Forecasting stock indices with back propagation neural network.Expert Systems with Applications, 2011, 38(11):14346-14355.
[22] Chin T L, Hsin Y Y.Empirical of the Taiwan stock index option price forecasting model-Applied artificial neural network.Applied Economics, 2011, 41(15):1965-1972.
[23] Pan Y C, Xiao Z, Wang X M, et al.A multiple support vector machine approach to stock index forecasting with mixed frequency sampling.Knowledge-Based Systems, 2017, 122:90-102.
[24] Ince H, Trafalis T B.Short term forecasting with support vector machines and application to stock price prediction.International Journal of General Systems, 2008, 37(6):677-687.
[25] 赛英,张凤廷,张涛.基于支持向量机的中国股指期货回归预测研究.中国管理科学, 2013, 21(3):35-39.(Sai Y, Zhang F T, Zhang T.Research of Chinese stock index futures regression prediction based on support vector machines.Chinese Journal of Management Science, 2013, 21(1):35-39.)
[26] Nti I K, Adekoya A F, Weyori B A.Efficient stock-market prediction using ensemble support vector machine.Open Computer Science, 2020, 10(1):153-163.
[27] Baek Y, Kim H Y.ModAugNet:A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module.Expert Systems with Applications, 2018, 113(12):457-480.
[28] Fischer T, Krauss C.Deep learning with long short-term memory networks for financial market predictions.European Journal of Operational Research, 2018, 270(2):654-669.
[29] Liu Y.Novel volatility forecasting using deep learning long short term memory recurrent neural networks.Expert Systems with Applications, 2019, 132:99-109.
[30] Yang H, Pan Z, Tao Q, et al.Robust and adaptive online time series prediction with long shortterm memory.Computational Intelligence and Neuroscience, 2017, 2017:1-9.
[31] Huang G B, Zhu Q Y, Siew C K.Extreme learning machine:A new learning scheme of feedforward neural networks.IEEE International Joint Conference on Neural Networks, 2004:2:985-990.
[32] Li X D, Xie H R, Wang R, et al.Empirical analysis:Stock market prediction via extreme learning machine.Neural Computing and Applications, 2016, 27(1):67-78.
[33] Wang M G, Zhao L F, Du R J, et al.A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms.Applied Energy, 2018, 220:480-495.
[34] Zhu Q Y, Qin A K, Suganthan P S, et al.Evolutionary extreme learning machine.Pattern Recognition, 2005, 38(10):1759-1763.
[35] Storn R, Price K.Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces.Journal of Global Optimization, 1997, 11(4):341-359.
[36] Kahneman D, Tversky A.Prospect theory:An analysis of decision under risk.Econometrica, 1979, 47(2):263-291.
[37] 赵汝为,熊熊,沈德华.投资者情绪与股价崩盘风险:来自中国市场的经验证据.管理评论, 2019, 31(3):50-60.(Zhao R W, Xiong X, Shen D H.Investor sentiment and stock price crash risk:Evidence from China.Management Review, 2019, 31(3):50-60.)
[38] 李文辉,王竟竟.基于不同规模指数的中国股票市场周内效应异质性.系统科学与数学,2021, 41(6):1682-1692.(Li W H, Wang J J.Heterogeneity of Day-of-the-Week effect in Chinese stock market based on different scale indexes.Journal of Systems Science and Mathematical Sciences, 2021, 41(6):1682-1692.)
[39] Alok K, Charles M C L.Retail investor sentiment and return comovements.The Journal of Finance, 2006, 61(5):2451-2486.
[40] 史永东,王谨乐.中国机构投资者真的稳定市场了吗?.经济研究, 2014, 49(12):100-112.(Shi Y D, Wang J L.Have Chinese institutional investors really stabilized the market.Economic Research Journal, 2014, 49(12):100-112.)
[41] 史永东,李竹薇,陈炜.中国证券投资者交易行为的实证研究.金融研究, 2009,(12):129-142.(Shi Y D, Li Z W, Chen W.An empirical study on the trading behavior of Chinese securities investors.Journal of Financial Research, 2009,(12):129-142.)
[42] 廖理,梁昱,张伟强.谁在中国股票市场中"博彩"?——基于个人投资者交易数据的实证研究.清华大学学报(自然科学版), 2016, 56(6):677-684.(Liao L, Liang Y, Zhang W Q.Who gambles in the Chinese stock market?-Evidence from individual investor trading data set.Journal of Tsinghua University (Science and Technology), 2016, 56(6):677-684.)
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