[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123559-en":3,"doc-seo-123559-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123559,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Investor Confidence and Forecastability of US Stock Market Realized Volatility - Evidence from Machine Learning","Using random forests, this study examines how investor confidence contributes to forecasting monthly aggregate realized stock-market volatility in the United States, beyond a broad set of macroeconomic and financial predictors. Models are estimated using data from 2001–2020 with forecast horizons up to one year, employing both recursive and rolling estimation windows. Findings show that investor confidence, particularly confidence uncertainty, delivers out-of-sample predictive power for overall realized volatility and its “good” and “bad” components, informing investors and policymakers.","Investor Confidence and Forecastability of US Stock Market Realized Volatility : Evidence from Machine Learning  \nRangan Guptaa , Jacobus Nel b and Christian Pierdziochc  \nFebruary 2021  \nAbstract  \nUsing a machine-learning technique known as random forests, we analyze the role of investor confidence in forecasting monthly aggregate realized stock-market volatility of the United States (US), over and above a wide-array of macroeconomic and financial variables. We estimate random forests on data for a period from 2001 to 2020, and study horizons up to one year by computing forecasts for recursive and a rolling estimation window. We find that investor confidence, and especially investor confidence uncertainty has out-of-sample predictive value for overall realized volatility, as well as its “good” and “bad” variants. Our results have important implications for investors and policymakers.  \nJEL classification: C22; C53; G10; G17  \nKeywords: Investor Confidence; Realized Volatility; Macroeconomic and Financial Predictors; Forecasting; Machine Learning  \na Department of Economics, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa; Email address: [rangan.gupta@up.ac.za](rangan.gupta@up.ac.za).  \nb Corresponding author. Department of Economics, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa; Email address: [neljaco380@gmail.com](neljaco380@gmail.com).  \nc Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany; Email [address: macroeconomics@hh.de](address: macroeconomics@hh.de).  \n1 Introduction  \nAs market agents tend to make overly optimistic or pessimistic judgments and choices, following the seminal contributions of Baker and Wurgler (2006, 2007), many other studies (see for example, Bathia and Bredin (2013), Gebka (2014), Da et al.,(2015), Huang et al. (2015), Jiang et al. (2019), Chen et al. (forthcoming)) have highlighted the role of investor sentiment in predicting (in-and out-of-sample) aggregate and firm-level (excess) stock returns of the United States (US) . Building on this area of behavioral finance, recent works (see for example, Gupta and Kyei (2016), Balcilar et al. (2018), Gupta (2019), Olson and Nowak (2019)) have also highlighted the role of investor sentiment in driving US stock market volatility. This second-moment effect isnot surprising due to the existence of so-called “noise traders\" in the market, who in turn, are investors whose trading decisions are based on what they perceive to be an informative signal but which, to a rational agent, does not convey any information (Black (1986)) . In the presence of such noise trading, equity prices tend to drift further from the fundamentals (Zhang (2006)), which then results in higher liquidity in terms of trading volume (Greene and Smart (1999)) and consequently higher risk, i.e., volatility, via the Mixture of Distribution Hypothesis (MDH) introduced by Clark (1973), or Sequential Information Arrival Hypothesis (SIAH) developed by Copeland (1976) . Recall that, the MDH postulates that the innovation on returns is a linear combination of the intraday return movements. The intraday return increment incorporates the number of information flows arrived into the market in a given day. Since the intraday price movement is random, daily returns follow a mixture of normally distributed random variables with the information flow into the market as a mixing variable. To sum up, daily price changes are driven by a set of information flow and the arrival of unexpected news is accompanied by the above average trading activity. On the other hand, the SIAH questions the instantaneous relationship as predicted by MDH and provides a different explanation. It argues that each trader observes the information signal differently at times and may not receive the information simultaneously, thereby generating a series of incomplete equilibria. Market equilibrium can be established provided that all traders r","cbCaib2ex71LoVEH","https://ap.wps.com/l/cbCaib2ex71LoVEH","pdf",157429,1,23,"English","en",105,"# Introduction\n## Behavioral finance and investor sentiment\n## Noise trading, liquidity, and volatility mechanisms\n## Forecastability and out-of-sample evaluation","[{\"question\":\"What machine-learning method is used to study volatility forecasting?\",\"answer\":\"The paper uses random forests to model and forecast monthly aggregate realized stock-market volatility.\"},{\"question\":\"What time period and forecast horizons are analyzed?\",\"answer\":\"Random forests are estimated using data from 2001 to 2020, and forecasts are evaluated for horizons up to one year using recursive and rolling estimation windows.\"},{\"question\":\"How does investor confidence relate to realized volatility in the results?\",\"answer\":\"Investor confidence—and especially investor confidence uncertainty—shows out-of-sample predictive value for overall realized volatility as well as its “good” and “bad” variants.\"}]","Investor Confidence and Forecastability of US Stock Market Realized Volatility - 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