Open Interest Research Articles

Analysis of Investors’ Prediction Potential Using Holt Winters Model and Artificial Neural Networks

ABSTRACT This study analyses the predictive power of six categories of investors who traded Nifty Index future contracts on the National Stock Exchange of India (NSE). Quality data were collected from The Securities and Exchange Board of India (SEBI). Investors’ predictive potential was estimated by analysing the effect of open interest and volume traded on volatility for each category. The Holt–Winters (HW) exponential smoothing model successfully captured seasonality and provided a satisfactory analytical model for linear forecast. Nonlinearity was captured by Artificial Neural Networks (ANN). A resilient backpropagation algorithm with backtracking was used to determine the weights triggered by a logistic activation function for smoothing neurons. The multilayer perceptron network was further trained for time series data on volatility considering volume and open interest as input neurons. Predictive powers were considered best for the method with the least Root Mean Square Error (RMSE). The results suggest that nonlinearity in the data was well captured by the ANN as the RMSE for the ANN was smaller compared to the RMSE for the HW model. The RMSE using ANN was least for Foreign Institutional Investors (FIIs) that suggests that FIIs have better prediction potential compared to other investors.

The COVID-19 pandemic, volatility, and trading behavior in the bitcoin futures market

This paper contributes to the literature on the coronavirus (COVID-19) pandemic impacts on the Bitcoin futures (BTCF) market and to the ongoing consideration of the dynamic relationship between volatility (or returns) and trading behavior variables, such as volume and open interest as a proxy for belief dispersion. This paper focuses on the role of the unprecedented market stress induced by the COVID-19 pandemic in the interrelations among the variables. Accordingly, this paper proposes a structural change (SC)-VAR-MGARCH model and finds the COVID-19 pandemic has initiated a significant regime change. Furthermore, the relationship between the variables in the pre-pandemic regime is notably unclear, whereas an increase in belief dispersion in the pandemic regime due to market stress reduces BTCF returns but raises trading volume and volatility evidently. The outcomes in the pandemic regime are remarkably consistent with the difference of opinions model, though existing evidence on the dynamic relations is ambiguous. Moreover, the outcomes support our hypothesis that, in addition to information flows, market stress causing traders’ behavioral biases should be considered as one of the crucial factors of tremendous price variability.

Uncovering the complex asymmetric relationship between trading activity and commodity futures price: Evidenced from QNARDL study

This study cracks the multidimensional asymmetric relationship between trading activity (volume and open interest) and commodity futures prices to analyze the short-term dynamics and long-term cointegrating relationship across different state of the market considering both positive and negative changes in trading activity using a novel Quantile Non-linear Autoregressive Distributed Lag (QNARDL) approach. First, the asymmetric price effect is found in short- and long-run of volume and open interest. Second, the asymmetric price effect due to positive and negative changes in open interest (volume) is found in the short-run (long-run) for copper (gold and crude) futures. Third, distributional asymmetry is found in the above two price effects on all three commodity futures implying that the price effect changes with changes in market conditions such bearish, bullish, and normal. Our findings will help the portfolio managers for effective investment and diversification decision, traders for better trading strategy, hedgers for better risk management strategy, and regulators and concerned exchange for effective policy making in varied market conditions.

A Tale of Two Premiums Revisited

This paper investigates the effect of the “financialization” of commodity markets in terms of pricing. I explore whether the emergence of commodity index traders affects weekly returns and turnover during the roll periods. I split the sample (1994–2017) into the pre-financialization (1994–2003) and the post-financialization (2004–2017). I directly test whether the market share of index traders contributes to commodity returns and whether risk adjustments (based on momentum, basis, basis-momentum, open interest, crowding, and average factors) alter liquidity and insurance premiums documented in Kang, Rouwenhorst, and Tang (2020). I also examine how the financialization affects liquidity and insurance premiums. Finally, since previous results are obtained with Fama-MacBeth regressions, I use an alternative method totest how liquidity and insurance premiums determine commodity returns.

Economic news and the cross-section of commodity futures returns

This paper examines whether the economic news pessimism risk factor is priced by the investors in the cross-section of commodity futures returns. Using a unique economic news dataset, we find that the pessimism risk factor is priced in a range of commodity portfolios. Our analysis reveals a strong asymmetric effect of news on the commodity futures excess returns, and commodities with low basis, low momentum, low open interest and high volatility are exposed to negative news risk. We show that trading on pessimistic news yields meaningful profits that are unexplained by the market, basis and momentum factors.

Speculation and informational efficiency in commodity futures markets

We use recent data of the CFTC to re-assess the effects of financial traders on informational efficiency in commodity futures markets. To do so, we focus on excessive volatility as a means to reflect noise in the price discovery process. We show that the role of financial traders on volatility is more complex than often assumed in the literature. Researchers should distinguish between the trading motives of market actors, as well as between increases and decreases in open interest. Several findings stand out. In particular, we find that short-term fluctuations in open interest might primarily be driven by speculators’ demand for liquidity, and that traditional speculators, as identified by the MM category of the CFTC, may be responsible for increasing volatility in several markets.

The American Politics of French Theory: Derrida, Deleuze, Guattari, and Foucault in Translation

The American Politics of French Theory: Derrida, Deleuze, Guattari, and Foucault in Translation

Commodity markets intervention: Consequences of speculation, and informed trading

The Chinese regulatory authority's interventions into the futures market for steel during 2016 provide a rare opportunity to study the unintended consequences of interventions. Although hot rolled coil (HRC) and rebar are products with nearly identical components, their futures contracts trade separately. The regulatory authority intervened into the futures market for rebar but refrained from doing so for HRC. We document impacts of interventions measured by increased volatility, trading volume, and spreads in rebar markets. There is also evidence of informed trading in both markets, reflected by abnormally high trading volumes and low open interests occurring the day before the announcement of interventions.

Does time-varying illiquidity matter for the Indian stock market? Evidence from high-frequency data

This article examines variations in illiquidity in the Indian stock market, using intraday data. Panel regression reveals prevalent day-of-the-week, month, and holiday effects in illiquidity across industries, especially during exogenous shock periods. Illiquidity fluctuations are higher during the second and third quarters. The ranking of most illiquid stocks varies, depending on whether illiquidity is measured using an adjusted or unadjusted Amihud measure. Using pooled quantile regression, we note that illiquidity plays an important asymmetric role in explaining stock returns under up- and down-market conditions in the presence of open interest and volatility. The impact of illiquidity is more severe during periods of extreme high and low returns. JEL Classification: G10, G12

The Effect of Promotion on Interest in Opening an Account at Bank Muamalat, Palu Branch

Purpose - The purpose of this study was to determine whether promotion has an effect on the interest of Islamic banking students in opening an account at the hammer branch of the muamalat bank and to find out how significant the effect of promotion is on student interest in opening an account at the hammer branch of the muamalat bank.Method - This study uses primary data, namely a questionnaire that uses a Likert scale as a calculation scale. The sample of this study was 485 students of the Faculty of Economics and Islamic Business IAIN Palu, Department of Islamic Banking from class 2016 to class 2018. To analyze the data, the authors used multiple linear regression methods and SPSS version 21. Result - Research result shows that the magnitude of the influence of the variable (X) on the variable (Y) is 0.388 or 38.8%. These results can provide an illustration that respondents (students) view that promotion can influence student interest in opening an account at a muamalat bank and promotion has a significant effect on student interest in opening an account at a muamalat bank. it can be seen that the promotion variable (X) has a value of tcount 3.783 t table 1.663 and has a significant value (sig) of 0.000 in the coefficients table with a value of α (significant level) 0.05. This means 0.000 0.05.Implications - This study is only limited to using data from several students in one university. Originality-. The difference with previous research is in the location and object under study.

FUNDAMENTAL & TECHNICAL ANAYSIS OF CRUDE OIL PRICES

The human mind is not as good at processing large amounts of information as we might like. Psychologists have shown that human beings are only able to juggle small numbers of related and often conflicting pieces of information without making judgment errors. As a result, individuals faced with the vast amounts of information available to support investment decisions often find themselves swamped by the enormity of the task; unable to see the wood from the trees. Technical analysis is a field of financial markets research that works to address the above problem by focusing on a single, commonly available, data source that reflects all known information and activity relating to all monetary securities- Price history. Technical analysts argue that as markets are efficient, prices reflect all known information and that they move over time as participants react to new information and changing needs.
 As a result, the technical analysis of these price changes can provide real insight into the market dynamics and be used to develop trade strategies that exhibit superior risk/reward characteristics. While technical analysis approaches have developed significantly over the past few decades, some techniques are far more ancient. While their real origins are anonymous, Japanese candlestick charts have been recorded as being employed in the rice markets as far back as the 1600s. What is particularly interesting is that various of these ancient approaches continue to provide highly effective trading signals when applied to modern markets and securities.
 Crude oil price volatility is in the midst of the largest business risk that oil and gas companies face. This is followed by unstable policy regime, managing costs and risks emerging from technological advancements. The high levels and rapid fluctuations of petroleum prices have become a great concern to individual consumers, firms, policy makers and society. Technical Analysis is the forecasting of future financial price movements based on an examination of past price movements. Like weather forecasting, technical analysis does not result in absolute predictions about the future. Instead, technical analysis can help investors anticipate what is "likely" to happen to prices over time. Technical analysis uses a wide variety of charts that show price over time. Hence, to mitigate the negative impacts of price volatility and to predict about the future price movement of crude oil and natural gas we can use technical analysis.
 Technical analysis is the study of market action, primarily through the use of charts, for the purpose of forecasting price trends. The term “market action” includes the three principal source of action available to the technician-price, volume and open interest. This research paper highlights fundamental factor which affects the Brent price and analysed the factor which are highly correlated with Brent price and on the basis of the results forecasted the Brent price for next five years. Fundamental analysis of Brent oil, price pattern & movement of crude oil has also been carried out using candlestick technical tool.

Quality of alternatives positively associated with interest in opening up a relationship

AbstractWe use the Investment Model framework to examine what relationship features are associated with interest in and positive evaluations of consensual non‐monogamy (CNM) among individuals in monogamous relationships. In data sets from the United States (Study 1), Europe (Study 2), and Korea (Study 3; total N = 886), perceptions of higher‐quality alternatives were consistently associated with more interest in CNM. Further, consistent with previous work on commitment‐motivated relationship maintenance processes, we found support for an indirect effect whereby lower commitment was associated with higher perceived attractiveness of alternatives, which in turn was associated with more interest in CNM. The data suggest that the idea of CNM is likely to be most attractive to those who see themselves as having higher‐quality relationship options.

Effect Open Interest on Implied Cost of Equity Capital of Firms Listed Single Stock Futures in the Derivatives Market

Effect Open Interest on Implied Cost of Equity Capital of Firms Listed Single Stock Futures in the Derivatives Market

The Role of Fleeting Orders on Options Expiration

We employ NASDAQ order level data to analyze intraday trading at option expirations and cross-market price pressure spillover. Algorithmic traders appear to place proportionately more fleeting orders in optionable stocks on option expiration versus non-expiration days. Since most observed fleeting orders are outside the NBBO, we discard the possibility that their purpose is to search for latent liquidity. Relation between NBBO proximity to strike prices and fleeting order direction implies they play a role in stock price clustering on option expiration days. We show that fleeting orders impact subsequent NBBO and that fleeting order direction is related to option Open Interest.

When Brokerages Restrict Retail Investors, Does the Game Stop?

We study a set of trading restrictions that limit equity and/or options positions imposed by retail-oriented broker-dealers in 38 stocks, including GameStop. Using brokerages’ capital requirements from the National Securities Clearing Corporation (NSCC) and the exact timing of restrictions, for identification, we find large stock price effects: CARs average -13.54% within two hours following a stock’s first trading restrictions and -51.97% after five trading days. When restrictions are lifted, share prices do not rebound. When traders substitute options for equities, options volume and open interest spike, as do implied volatilities, which rise more than realized volatilities. Options purchasers overpay, creating large transfers to options sellers and market-makers. Margin increases show similarities and differences relative to trading restrictions.

Measuring gambling activity in options market

PurposeThis paper investigates how the gambling measure captures market bubble events, and how it predicts stock return and option return.Design/methodology/approachThis paper proposes a gambling activity measure by jointly considering open interest and moneyness of out-of-the-money (OTM) individual equity call options.FindingsThe new measure, CallMoney, captures excessive optimism during the dot-com bubble, the oil price bubble and the pre-GFC stock market bubble. CallMoney robustly and negatively predicts both OTM and at-the-money call option returns cross-sectionally. The option return predictability of CallMoney is stronger when stock price is further from its 52-weeks high, capital gains overhang is lower, and when information uncertainty of the underlying stock is higher. CallMoney also robustly and negatively predicts cross-sectional stock returns.Originality/valueThe gambling measure has the advantages of being economically intuitive, model-free, easy to measure. The measure performs more robustly than existing lottery measures with respect to option and stock return predictability and more reliably captures the overpricing of options and stocks. The work helps understanding the gambling related anomalies in equity option returns and stock returns.

Stock market signals and consequences of securities class actions lawsuits: a microstructure perspective

We perform a microstructure analysis of trading activities pre- and post-class period end-dates of securities class action lawsuits. We posit that these events are likely to significantly impact the spreads of affected firms, in addition to the well documented market capitalization loss that spurs the legal action. We detect neither meaningful widening of spreads nor change in put-call ratios ahead of the class period end-date, suggesting no microstructure or option open interest signal of the upcoming event. However, we present strong evidence of a significant degradation in market quality post the class period end-date based on widening spreads lasting for at least 60 trading days. We also document a trading volume spike and share price decline around the event date. Our research shows the impact on shareholders extends beyond the capitalization loss through wider spreads for defendant firms, while the same is not true for a control sample.

Early Results on Depth of the Nascent Kenyan Derivative Market

Despite their importance in hedging against risk and reducing price uncertainty, derivative markets remain undeveloped or absent in many African countries. This paper describes market depth using key trends observed in the Kenyan derivatives market for the first 30 weeks of trading using mixed methods. Market depth was measured by the number of open interests of 142 trading days (30 weeks. The market was described using trend analysis, tests of means, and thematic analysis. The results revealed a market highly dominated by one company's single stock futures (Safaricom Plc), whose overall trade was 68% of the 6179 open contracts. Further, the market has strong weekly swings fluctuating from no trade to a high of 326 and a weekly average of 206 contracts. The market segment of single stock futures is significantly deeper than that of equity index futures. The qualitative study attributed the results to limited knowledge on derivatives amongst investors, unclear market policies, few derivatives products, and skepticism associated with developing financial markets. The Nairobi Securities Exchange (NSE) is advised to intensify investor education, introduce market makers, add new derivatives products, and transform the Nairobi Securities Exchange Clearing House into a full Central Counterparty (CCP) structure to accelerate market depth. This will create a pathway to market depth through efficiency and reduction of operational risks.

Determinants of the WTI‐Brent price spread revisited

AbstractUsing autoregressive distributed lag modeling and structural break testing, we explore the drivers of the oil price spread between West Texas Intermediate and Brent in a data set from 1995 to 2019. We find a major structural break in December 2010 and minor breaks in 2005 and 2012. Important spread determinants are the convenience yield, as a proxy for crude oil inventories, the trading activity in crude oil paper markets, shipping costs, as well as the stock market development in the United States and Europe. After the break in 2010, the paper market activity, open interest, and shipping costs have become more important spread drivers.

Analysis of Volatility Volume and Open Interest for Nifty Index Futures Using GARCH Analysis and VAR Model

The generalized autoregressive conditional heteroscedastic model (GARCH) is used to estimate volatility for Nifty Index futures on day trades. The purpose is to find out if a contemporaneous or causal relation exists between volatility volume and open interest for Nifty Index futures traded on the National Stock Exchange of India, and the extent and direction of these relationships. A complete absence of bidirectional causality in any particular instance depicts noise trading and empirical analysis according to this study establishes that volume has a stronger impact on volatility compared to open interest. Furthermore, the impulse originating from volatility of volume and open interest is low.