scholarly journals Measures Of Investor Sentiment: A Comparative Analysis Put-Call Ratio Vs. Volatility Index

2011 ◽  
Vol 6 (8) ◽  
Author(s):  
Arindam Bandopadhyaya ◽  
Anne Leah Jones
Keyword(s):  
Asset Prices ◽  
Investor Sentiment ◽  
Economic Factors ◽  
Short Term ◽  
Market Sentiment ◽  
Volatility Index ◽  
Daily Data ◽  
Traditional Research ◽  
Two Measures ◽  
Better Than

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">Traditional research on asset pricing has focused on firm-specific and economy-wide factors that affect asset prices.<span style="mso-spacerun: yes;">&nbsp; </span>Recently, the finance literature has turned to non-economic factors, such as investor sentiment, as possible determinants of asset prices (see for example, Fisher and Statman 2000 and Baker and Wurgler 2006).<span style="mso-spacerun: yes;">&nbsp; </span>Studies such as Baek, Bandopadhyaya and Du (2005) suggest that shifts in investor sentiment may explain short-term movements in asset prices better than any other set of fundamental factors.<span style="mso-spacerun: yes;">&nbsp; </span>A wide array of investor sentiment measures are now available, which leads us quite naturally to the question of which measure best mirrors actual market movements.<span style="mso-spacerun: yes;">&nbsp;&nbsp; </span>In this paper, we begin to address this question by comparing two measures of investor sentiment which are computed daily by the Chicago Board Options Exchange (CBOE) and for which historical data are freely available on the CBOE website, thus making them ideal for use by both academics and practitioners studying market behavior: the Put-Call Ratio (PCR) and the Volatility Index (VIX).<span style="mso-spacerun: yes;">&nbsp; </span>Using daily data from January 2, 2004 until April 11, 2006, we find that the PCR is a better explanatory variable than is the VIX for variations in the S&amp;P 500 index that are not explained by economic factors.<span style="mso-spacerun: yes;">&nbsp; </span>This supports the argument that, if one were to choose between these two measures of market sentiment, the PCR is a better choice than the VIX.</span></span></p>

scholarly journals Behind the Volatility Index Levels: The Paradox of 2016

2017 ◽  
Vol 1 (1) ◽  
pp. 44
Author(s):  
G. D. Hancock
Keyword(s):  
Strong Evidence ◽  
Abnormal Returns ◽  
Investor Sentiment ◽  
Market Volatility ◽  
Volatility Index ◽  
Daily Data ◽  
Vix Futures ◽  
Negative News ◽  
2016 Election ◽  
Time Periods

The low 2016 volatility index levels present a paradox in light of previous research suggesting periods of uncertainty and negative news events should reflect higher VIX levels. This study uses daily data for the VIX, VIX futures and the VVIX, to examine the information content of variations in the natural logarithmic changes in the index levels relative to 12 other parallel time periods encompassing 2004-2016. Straight-forward variation and predictive tests are constructed to determine signs of unusual market volatility behavior. The results reveal strong evidence of unusual volatility behavior during the 2016 election period, pocked by frequent periods of abnormal returns. The 2016 VIX levels alone are shown to be insufficient to draw conclusions regarding investor sentiment.

scholarly journals Incorporating Research Reports and Market Sentiment for Stock Excess Return Prediction: A Case of Mainland China

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Huilin Song ◽  
Diyun Peng ◽  
Xin Huang
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Learning Algorithm ◽  
Mainland China ◽  
Investor Sentiment ◽  
Excess Return ◽  
Research Report ◽  
Market Sentiment ◽  
Stock Price Prediction ◽  
Better Than

The prediction of stock excess returns is an important research topic for quantitative trading, and stock price prediction based on machine learning is receiving more and more attention. This article takes the data of Chinese A-shares from July 2014 to September 2017 as the research object, and proposes a method of stock excess return forecasting that combines research reports and investor sentiment. The proposed method measures individual stocks released by analysts, separates the two indicators of research report attention and rating sentiment, calculates investor sentiment based on external market factors, and uses the LSTM model to represent the time series characteristics of stocks. The results show that (1) the accuracy and F1 evaluation indicators are used, and the proposed algorithm is better than the benchmark algorithm. (2) The performance of deep learning LSTM algorithm is better than traditional machine learning algorithm SVM. (3) Investor sentiment as the initial hidden state of the model can improve the accuracy of the algorithm. (4) The attention of the split research report takes the two indicators of investor sentiment and price as the input of the model, which can effectively improve the performance of the model.

scholarly journals Empirical Evidence of Co-Movement between the Canadian CDS, Stock Market And TSX 60 Volatility Index

2020 ◽  
Vol 3 (3) ◽  
pp. 51-64
Author(s):  
Ramzan Ali ◽  
Usman Ullah Butt ◽  
Muhammad Musa Khan ◽  
Muhammad Shaheer ◽  
Farhan Abbas Zaidi
Keyword(s):  
Stock Market ◽  
Credit Default Swaps ◽  
Medium Term ◽  
Short Term ◽  
Volatility Index ◽  
Daily Data ◽  
Credit Default ◽  
Long Time ◽  
Prime Objective

Purpose- The prime objective of this study was to find the co-movement between the Canadian credit default swaps market, the Stock market and volatility index (TSX 60 Index) Design/ Methodology- To achieve this purpose, daily data containing 2870 observations starting from the 1st of January 2009 to the 30th of December 2019 were analyzed. This study employed the wavelet approach to present results in short-term, medium-term, long-term, and very long time. Findings- The findings of this study showed a negative correlation between the CDS market, stock market, and the TSX 60 index in the short-term as well as in the long-term term, while in medium-term and very long-term period correlation is strongly positive. The wavelet co-movement results in the short-term and long-term were negative, while this relationship in the medium-term and very long-term period was strongly positive. Practical Implications- This research provides simultaneous valuable information for investment decisions in the short, medium, and long term time horizons, as well as for the policymakers in the Canadian credit default swaps market, stock market, and the volatility index (TSX 60 Index).

scholarly journals Gait Stability Measurement by Using Average Entropy

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 412
Author(s):  
Han-Ping Huang ◽  
Chang Francis Hsu ◽  
Yi-Chih Mao ◽  
Long Hsu ◽  
Sien Chi
Keyword(s):  
Postural Stability ◽  
Multiscale Entropy ◽  
Gait Stability ◽  
Eyes Closed ◽  
Stride Interval ◽  
Step Interval ◽  
Stability Measurement ◽  
Eyes Open ◽  
Two Measures ◽  
Better Than

Gait stability has been measured by using many entropy-based methods. However, the relation between the entropy values and gait stability is worth further investigation. A research reported that average entropy (AE), a measure of disorder, could measure the static standing postural stability better than multiscale entropy and entropy of entropy (EoE), two measures of complexity. This study tested the validity of AE in gait stability measurement from the viewpoint of the disorder. For comparison, another five disorders, the EoE, and two traditional metrics methods were, respectively, used to measure the degrees of disorder and complexity of 10 step interval (SPI) and 79 stride interval (SI) time series, individually. As a result, every one of the 10 participants exhibited a relatively high AE value of the SPI when walking with eyes closed and a relatively low AE value when walking with eyes open. Most of the AE values of the SI of the 53 diseased subjects were greater than those of the 26 healthy subjects. A maximal overall accuracy of AE in differentiating the healthy from the diseased was 91.1%. Similar features also exists on those 5 disorder measurements but do not exist on the EoE values. Nevertheless, the EoE versus AE plot of the SI also exhibits an inverted U relation, consistent with the hypothesis for physiologic signals.

scholarly journals A Novel LSTM Model with Interaction Dual Attention for Radar Echo Extrapolation

2021 ◽  
Vol 13 (2) ◽  
pp. 164
Author(s):  
Chuyao Luo ◽  
Xutao Li ◽  
Yongliang Wen ◽  
Yunming Ye ◽  
Xiaofeng Zhang
Keyword(s):  
Short Term Memory ◽  
Weather Forecast ◽  
Vital Role ◽  
Data Sets ◽  
Short Term ◽  
Learning Techniques ◽  
Radar Echo ◽  
Hidden States ◽  
Better Than

The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.

scholarly journals Can the Chinese volatility index reflect investor sentiment?

2021 ◽  
Vol 73 ◽  
pp. 101612
Author(s):  
Wen Long ◽  
Manyi Zhao ◽  
Yeran Tang
Keyword(s):  
Investor Sentiment ◽  
Volatility Index

An investor sentiment barometer — Greek Implied Volatility Index (GRIV)

2012 ◽  
Vol 23 (2) ◽  
pp. 77-93 ◽  
Author(s):  
Costas Siriopoulos ◽  
Athanasios Fassas
Keyword(s):  
Implied Volatility ◽  
Investor Sentiment ◽  
Volatility Index

Is investor sentiment a relevant factor in determining asset prices?

2018 ◽  
Vol 47 (3) ◽  
pp. 243-257 ◽  
Author(s):  
Kamini Solanki ◽  
Yudhvir Seetharam
Keyword(s):  
Asset Prices ◽  
Investor Sentiment ◽  
Relevant Factor

scholarly journals Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume

2021 ◽  
Author(s):  
Nino Antulov-Fantulin ◽  
Tian Guo ◽  
Fabrizio Lillo
Keyword(s):  
Econometric Models ◽  
Gradient Boosting ◽  
Point Estimate ◽  
Order Book ◽  
Probabilistic Forecasting ◽  
Short Term ◽  
Ensemble Models ◽  
Gradient Boosting Machine ◽  
Different Sources ◽  
Better Than

AbstractWe study the problem of the intraday short-term volume forecasting in cryptocurrency multi-markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble, capable of adaptively exploiting, for the forecasting, different sources of data and providing a volume point estimate, as well as its uncertainty. We provide evidence of the clear outperformance of our model with respect to econometric models. Moreover our model performs slightly better than Gradient Boosting Machine while having a much clearer interpretability of the results. Finally, we show that the above results are robust also when restricting the prediction analysis to each volume quartile.

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