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A Linear Process Approach to Short-Term Trading Using the VIX Index As a Sentiment Indicator

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Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 July 2021 Article A Linear Process Approach to Short-term Trading Using the VIX Index as a Sentiment Indicator Yawo Mamoua Kobara 1,‡ , Cemre Pehlivanoglu 2,‡* and Okechukwu Joshua Okigbo 3,‡ 1 Western University; [email protected] 2 Cidel Financial Services; [email protected] 3 WorldQuant University; [email protected] * Correspondence: [email protected] ‡ These authors contributed equally to this work. 1 Abstract: One of the key challenges of stock trading is the stock prices follow a random walk 2 process, which is a special case of a stochastic process, and are highly sensitive to new information. 3 A random walk process is difficult to predict in the short-term. Many linear process models that 4 are being used to predict financial time series are structural models that provide an important 5 decision boundary, albeit not adequately considering the correlation or causal effect of market 6 sentiment on stock prices. This research seeks to increase the predictive capability of linear process 7 models using the SPDR S&P 500 ETF (SPY) and the CBOE Volatility (VIX) Index as a proxy for 8 market sentiment. Three econometric models are considered to forecast SPY prices: (i) Auto 9 Regressive Integrated Moving Average (ARIMA), (ii) Generalized Auto Regressive Conditional 10 Heteroskedasticity (GARCH), and (iii) Vector Autoregression (VAR). These models are integrated 11 into two technical indicators, Bollinger Bands and Moving Average Convergence Divergence 12 (MACD), focusing on forecast performance. The profitability of various algorithmic trading 13 strategies are compared based on a combination of these two indicators. This research finds 14 that linear process models that incorporate the VIX Index do not improve the performance of 15 algorithmic trading strategies. 16 Keywords: Short-term trading, mean reversion, VIX, SPY, linear stochastic process, MACD, 17 Bollinger Bands Citation: Kobara, Y.; Pehlivanoglu, C.; Okigbo, O.. 2021. Title. International Journal of Financial 18 1. Introduction Studies 1: 0. https://doi.org/ 19 The practice of stock trading relies mainly on technical indicators that are built 20 upon historical prices of an underlying security. As historical prices follow a random Received: 21 walk process, which is a special case of a stochastic process, it is difficult to make price Accepted: 22 predictions in the short-term. In order to make more accurate predictions, traders Published: 23 need to consider the market trends as well as the trend of an underlying security. The 24 current market trends are heavily influenced by the advancements in technology and Publisher’s Note: MDPI stays neu- 25 the subsequent increase in the use of social media. Now more than ever, short-term tral with regard to jurisdictional 26 claims in published maps and insti- stock price movements are driven by a type of social anxiety called "Fear of missing tutional affiliations. 27 out" (FOMO), fuelled by the news and social media (Khan 2021). A trading atmosphere 28 primarily driven by emotional factors such as fear and greed reduces the efficacy and 29 accuracy of common technical indicators built on an underlying process, thereby leading Copyright: © 2021 by the authors. 30 to the erosion of profits. This calls for investigating the influence of market sentiment on Submitted to Int. J. Financial Stud. 31 short-term trading. for possible open access publication 32 While numerous methods exist for market sentiment analysis, such as Twitter under the terms and conditions 33 sentiment analysis (Souza et al. 2015), relying on data collected from third parties and of the Creative Commons Attri- 34 requiring additional modeling, the scope of this research is the CBOE Volatility (VIX) bution (CC BY) license (https:// 35 Index as a proxy for market sentiment. This paper argues that, as a leading indicator, the creativecommons.org/licenses/by/ 36 VIX Index improves the short-term exit and entry signal precision. A high VIX generally 4.0/). Version July 28, 2021 submitted to Int. J. Financial Stud. https://www.mdpi.com/journal/ijfs © 2021 by the author(s). Distributed under a Creative Commons CC BY license. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 July 2021 Version July 28, 2021 submitted to Int. J. Financial Stud. 2 of 21 37 indicates that investors are weary of the current market environment and are purchasing 38 insurance contracts to hedge their portfolios in anticipation of a drawdown. Whereas, 39 a low VIX suggests complacency and that any movement in the stock market will be 40 restrained. 41 This paper seeks to answer the question of whether the incorporation of the VIX 42 index improves the performance of a mean reversion trading strategy. It proposes a 43 two-step approach: the first step is to fit a linear process model with the multivariate 44 time series including the VIX Index; and the second step is to integrate the linear 45 process model into day trading with technical indicators such as Bollinger Bands and 46 Moving Average Convergence Divergence (MACD). It uses intraday data from Refinitiv 47 DataScope and works with the 15-second dataset of the SPDR S&P 500 (SPY) ETF 48 (including the Open, High, Low, Close prices, as well as the Volume) and the VIX Index 49 between January 1, 2019 and December 31, 2020. 50 2. THEORETICAL FRAMEWORK 51 The literature surrounding short-term trading and market sentiment is immense 52 and varied. This section provides a literature review of the following: (i) the relationship 53 between market sentiment and trading signals; (ii) the mean-reversion methods for 54 short-term trading; and (iii) Bollinger Bands. 55 2.1. Market Sentiment 56 Sentiment analysis is a growing area of interest for financial analysts and investors. 57 There is an increasing body of literature in behavioral finance that investigates the impact 58 of sentiment on retail and institutional investors as well as financial market dynamics. 59 Kearney and Liu(2014) identifies two types of sentiment: (i) market sentiment and 60 (ii) text-based sentiment or textual sentiment. Market sentiment is the "beliefs about 61 future cash flows and investment risks that are not justified by the facts at hand (Kearney 62 and Liu 2014; Baker and Wurgler 2007) but based on "the subjective judgments and 63 behavioral characteristics of investors" (Kearney and Liu 2014). Text-based sentiment is 64 the "the degree of positivity or negativity in texts" (Kearney and Liu 2014). It may not 65 have strictly positive-negative binary effects, but also includes other ones such as strong- 66 weak and active-passive. As such, it includes a "more objective reflection of conditions 67 within firms, institutions and markets” (Kearney and Liu 2014). In addition, Kearney 68 and Liu(2014) and Li(2006) argue to include information received from text-based 69 sentiments in modern econometric models. 70 Souza et al.(2015) use Twitter sentiment analytics in their investigation for a possible 71 significant relationship between Twitter sentiment information and financial market 72 dynamics - stock returns, volatility, and volume (Souza et al. 2015). Their results indicate 73 that the social media is a valuable source of information, especially in the retail sector ( 74 Souza et al. 2015). On that account, Twitter real-time information "if properly modelled, 75 can provide ex-ante information about the market even before the main news wires ( 76 Souza et al. 2015)." 77 Moreover, prominent institutions have developed tools and methods to measure 78 and quantify market sentiment. These include social media sentiment analysis, Chicago 79 Board Options Exchange (CBOE) Volatility Index (VIX) (Bekiros and Georgoutsos 2008; 80 Li 2006; Corrado and Miller 2005; Fernandes et al. 2014), NYSE Bullish percentage ( 81 Clarke and Statman 1998), Baker and Wurgler sentiment index (BW index) (Baker and 82 Wurgler 2006), Buffet Indicator Chang and Pak(2018); Mislinski(2021), etc. 83 On September 22, 2003, the Chicago Board Options Exchange (CBOE) introduced a 84 new CBOE Volatility (VIX) Index, replacing the older volatility index. The CBOE VIX 85 calculation is based on the prices of a batch of (OTM) out-of-the-money and (NTM) 86 near-the-money put and call options on the S&P 500 index (SPY). Thus, CBOE Volatility 87 Index (VIX) "is simply the price of a linear portfolio of options." (Li 2006) The VIX Index 88 is given by Nielsen(Nielsen): Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 July 2021 Version July 28, 2021 submitted to Int. J. Financial Stud. 3 of 21 2 n DK 1 F 2 2 = i RT ( ) − − s ∑ 2 e Q Ki 1 (1) T i Ki T K0 89 where: s = VIX/100 ) VIX = s × 100, T = expiration time, F = forward index th 90 level based on index option prices, K0 = first strike below F, Ki = strike price of i 91 out-of-money option; a call if Ki > K0, put if Ki < K0; both call and put if Ki = K0, DKi = 92 interval between strike prices - half the difference between the strike on either side of ki, 93 such that k + − k − Dk = i 1 i 1 (2) i 2 94 R = risk-free interest rates to expiration, and Q(Ki) = the average of the bid quote 95 and ask quote for each option with strike Ki. 96 Lei et al.(2012) explore the relationship between the VIX as a proxy for market 97 sentiment and trading volume. VIX measures the expected 30-day implied volatility, 98 and is often referred to as "investor fear gauge". The authors find empirical evidence 99 to suggest that increases in the VIX Index explain the percentage increase in trading 100 volume, but only during a high VIX period.
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