Future Price Changes Research Articles

Investment and operating choice: Oil and natural gas futures prices and drilling activity

We present evidence that changes in oil and natural gas field investment measured by drilling rig use respond positively to changes in the futures prices of oil and natural gas, consistent with predictions based upon value-maximizing behavior. These results hold for world regions dominated by private independent oil companies but not national oil companies. In those cases where futures price changes are identified as drivers, the role of spot prices is either absent or weak. The results are robust to several alternative specifications including controls for changes in rig productivity.

An Application of Artificial Neural Networks and Fuzzy Logic on the Stock Price Prediction Problem

The financial industry has been becoming more and more dependent on advanced computing technologies in order to maintain competitiveness in a global economy. Hence, the stock price prediction problem using data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. Fuzzy logic (FL) and Artificial Neural Network (ANN) present an exciting and promising technique with a wide scope for the applications of prediction. There is a growing interest in both fields of fuzzy logic computing and the financial world in the use of fuzzy logic to predict future changes in prices of stocks, exchange rates, commodities, and other financial time series. Fuzzy logic provides a way to draw definite conclusions from vague, ambiguous or imprecise information. Artificial Neural Network is one of data mining techniques being widely accepted in the business area due to its ability to learn and detect relationships among nonlinear variables. The ANN outperforms statistical regression models and also allows deeper analysis of large data sets, especially those that have the tendency to fluctuate within a short of time period. In this paper, we investigate the ability of Fuzzy logic and multilayer perceptron (MLP), which is a kind of the ANN, to tackle the financial time series stock forecasting problem. The proposed approaches were tested on the historical price data collected from Yahoo Finance with different companies. Furthermore, the comparison between those techniques is performed to examine their effectiveness.

Does Internet Search Intensity Predict House Prices in Emerging Markets? A Case of India

We examine whether internet search intensity, as captured by Google’s Search Volume Index (SVI), predicts house price changes in an emerging market like India. Emerging markets have low internet penetration and high information asymmetry with a dominant unorganized real estate market. Like in developed markets such as the US, we find that internet search intensity strongly predicts future house price changes. A simple rebalancing strategy of buying a representative house in the city with the greatest change in search intensity and selling a representative house in the city with the smallest change in search intensity each quarter yields an annualized excess (over risk-free government t-bills) return of 4 percent.

Stock Options and Compensation

This note is a brief introduction to the logic and practice of using stock options to compensate executives. The Black-Scholes option-pricing model is used to estimate the value of option grants for a company. The note affords the opportunity to apply options valuation in the context of executive compensation, and serves as a companion to introductions to options that expose the reader to the Black-Scholes option-pricing model. A spreadsheet is available for student preparation of the case. Excerpt UVA-F-1521 Rev. Aug. 5, 2015 Stock Options and Compensation One of the major forms of executive compensation has been stock options. Use of stock options has been promoted as a way to help align incentives for shareholders and managers. That alignment is accomplished by linking executive compensation to performance for shareholders, which, in this instance, is reflected in the value of the company's stock. An alternative way to help align incentives is to give managers shares of company stock. Some firms do this, giving managers shares of stock with restrictions on managers' ability to sell the shares. A criticism of this approach is that it is not sufficiently forward-looking. Because the share price reflects the value of assets in place plus the value of future growth, when the manager gets shares, he or she receives the value of past decisions, and future price changes have only a marginal impact on the value of the shares. Moreover, with an option that gives the manager the right in the future to purchase stock at today's price, the manager gains only if the stock price increases over the current price. Generally, stock-option plans take three forms. A fixed-value plan gives a manager some fixed proportion of salary in the form of options. An example would be an executive whose salary is $ 500,000 and who is given a grant of options equal in value to half of the salary each year. A fixed number plan gives a manager a fixed number of options each year. For example, the plan might award the manager 5,000 at-the-money call options each year. Finally, there is the megagrant. Here, the executive receives a large grant of options this year and then none in the future. In all these forms, the intent is to align management incentives with those of shareholders. . . .

A Hidden Markov Model inference approach to testing the Random Walk Hypothesis: Empirical evidence from the Nigerian Stock Market

The movement of stock prices, in capital markets across the world, has been found to be both random and non-random. Basically, for a stock price to follow a random walk, its future price changes randomly based on all currently available information in the stock market, its price history inclusive. Some research findings have shown that the existing traditional unit root tests have low statistical power and hence cannot capture gradual changes over successive observations. Consequently, there is a need to revisit the random walk theory in stock prices using other tests. This study employs a Hidden Markov Model (HMM) with time-varying parameters to assess whether the stock price movements of the Nigerian Stock Exchange (NSE) follow a random walk process, or otherwise. Via hidden states, the HMM allows for periods with different volatility levels characterised by the hidden states. By simply accounting for the non-constant variance of the data with a two-state Hidden Markov Model and taking estimation into account via the Sequential Monte Carlo Expectation Maximisation (SMCEM) technique, this study finds no support of randomness. In conclusion, the stock price movements of the NSE do not follow the random walk process.

Chart Pattern in Climate Time Series Data

The application of chart pattern, or technical, analysis to climate time series data is demonstrated. The technique is known in the field of finance to perform predictions of future changes in prices. It is shown that global average temperature anomalies (HadCRUT4) clearly formed an ascending triangle chart pattern during the period of global warming hiatus since 1998. Even feature details of the chart pattern have been found, including the steep increase which has been observed recently. Relevance and benefit of the chart pattern analysis to research in the global climate system -- or complex physical systems in general -- are discussed.

When Future Change Matters: Modeling Future Price and Diffusion in Health Technology Assessments of Medical Devices

BackgroundHealth technology assessments (HTAs) that take account of future price changes have been examined in the literature, but the important issue of price reductions that are generated by the reimbursement decision has been ignored. ObjectivesTo explore the impact of future price reductions caused by increasing uptake on HTAs and decision making for medical devices. MethodsWe demonstrate the use of a two-stage modeling approach to derive estimates of technology price as a consequence of changes in technology uptake over future periods on the basis of existing theory and supported by empirical studies. We explore the impact on cost-effectiveness and expected value of information analysis in an illustrative example on the basis of a technology in development for preterm birth screening. ResultsThe application of our approach to the case study technology generates smaller incremental cost-effectiveness ratios compared with the commonly used single cohort approach. The extent of this reduction in the incremental cost-effectiveness ratio depends on the magnitude of the modeled price reduction, the speed of diffusion, and the length of the assumed technology life horizon. Results of value of information analysis are affected through changes in the expected net benefit calculation, the addition of uncertain parameters, and the diffusion-adjusted estimate of the affected patient population. ConclusionsBecause modeling future changes in price and uptake has the potential to affect HTA outcomes, modeling techniques that can address such changes should be considered for medical devices that may otherwise be rejected.

The Forecasting Power of the Volatility Index: Evidence from the Indian Stock Market

<div><p><em>Stock market volatility is a measure of risk in investment and it plays a key role in securities pricing and risk management. </em><em>The paper empirically analyzes the relationship between India VIX and volatility in Indian stock market. </em><em>India VIX is a measure of implied volatility which reflects markets’ expectation of future short-term stock market volatility.</em><em> It is a volatility index based on the index option prices of Nifty. </em><em>The study is based on time series data comprising of daily closing values of CNX Nifty 50 index comprising of 1656 observations from March 2009 to December 2015. </em><em>The results of the study </em><em>reveal that India VIX has predictive power for future short-term stock market volatility. It has higher forecasting ability for upward stock market movements as compared to downward movements. Therefore, it is more a bullish indicator. Moreover, the accuracy of forecasts provided by India VIX is higher for low magnitude future price changes relative to higher stock price movements. The current value of India VIX is found to be affected by past period volatility up to one month and it has forecasting ability for next one-month’s volatility which means the volatility in the Indian stock markets can be forecasted for up to 60 days period. </em></p></div>

Risk-Averse Dynamic Arbitrage in Illiquid Markets

Arguments on the existence of dynamic arbitrage and price manipulation strategies are often invoked to guide modeling price impacts of large trades. We revisit the concept of dynamic arbitrage in illiquid markets in the presence of time-varying stochastic price impact functions and a broad class of market price dynamics. We first establish a sufficient condition under which searching in the space of F0-measurable admissible round-trip trades is enough to attain a no-dynamic arbitrage certificate. This result simplifies identifying price impact structures that rule out dynamic arbitrage and accredits the analysis in some existing literature, where its assessment is limited to the search in the set of F0-measurable round-trip trades. For time-varying stochastic linear price impact functions, we show that this condition is necessary and sufficient for the absence of dynamic arbitrage. The present quantitative analysis implies that a trader’s opinion on the existence of dynamic arbitrage opportunities for a price impact model depends on his belief about expected future price changes and expected future price impacts, which can be revised over time by the collection of new information. This motivates us to let the existence of such arbitrage opportunities not only depend on the investor’s belief on expected price movements but also depend on the trader’s risk attitude. We thus introduce the concept of risk-averse dynamic arbitrage using a general time-consistent dynamic risk measure and a risk-aversion threshold level. Similar sufficient conditions are studied under which searching in the space of static round-trip trades enables us to conclude on no-risk-averse dynamic arbitrage.

Random Walk, Ergodicity versus Predictability – The Case of the Budapest Stock Exchange

In financial markets, the term 'random walk' is frequently used in relation to price movement over a period of time. This highly expressive term simply means that prices do not follow a predictable trend, and so previous movements are unsuitable as a basis for speculation regarding future price changes. There exists, however, another model which is based on the ergodic theorem, and this says that past and present probability distribution define the probability distribution which will dictate future market prices. Clearly, the 'random walk' hypothesis and the ergodic theorem are polar opposites, and, whilst the concept of uncertainty is closely linked to the former, the latter suggests that forecasting is, in fact, possible. This paper examines how the theory of efficient markets and the efficiency of the market itself provide the means to resolve this contradiction. We provide empirical proof concerning the 'random walk' theory, for both the recession and post-recession periods in the case of the stock index of the Hungarian stock market.

Finansal Risklerin Yönetilmesinde Türev Ürünlerin Kullanımı: Borsa İstanbul (Bist) 100 Endeksi ndeki Şirketler Üzerine Bir Araştırma (Usage Derivatives In Management Financial Risks: A Study On Firms In Borsa Istanbul (Bist) 100 Stock Index)

Firms that experience risks as a result of their activities, use various tools to manage these risks and be protected against them. One of the most significant tools used for protection against financial risks is derivatives. Derivatives used by firms to eliminate the financial risks caused by uncertainty, future price changes and other factors, contribute to diminish fluctuations on future prices and market mechanism to operate in a reliable environment. In this study, after informing about primary derivative products, research is focused on the level of usage of derivatives against E. Yilmaz – T. Aslan 8/1 (2016) 663-678 Isletme Arastirmalari Dergisi Journal of Business Research-Turk 664 risks caused by financial tools by firms registered on BIST 100 stock index operating in other than financial sector and which derivative products are being used by mentioned firms. In the result, it is founded that in 2013, 36 % of the firms and in 2014, 45 % of the firms in the context of the study use derivative products against financial risks. It is also founded that firms use swap contracts against interest risks, and futures contracts are preferred by firms against exchange risk. In addition to this, option contracts are mostly used against other price risks.

Measuring expectations from household surveys: new results on subjective probabilities of future house prices

I analyze new data on subjective probabilistic expectations on house prices collected in the Spanish Survey of Household Finances. Households are asked to distribute ten points among five different scenarios for the change in the price of their homes over the next 12 months. This paper is the first empirical study to document the beliefs of a representative sample of households about the future value of their homes. It also reviews the methodology of expectation measurement and recent work on household subjective probabilities. I model individual subjective probability densities using splines, construct quantiles from those densities, and analyze how the heterogeneity in the individual distributions relates to differences in housing and household characteristics. An important result of the paper is that women are more optimistic about the evolution of house prices than men. Location at the postal code level accounts for a large fraction of the variation in the subjective distributions across households. Finally, I provide some results on how subjective expectations matter for predicting spending behavior. Housing investment and car purchases are negatively associated with pessimistic expectations about future house price changes and with uncertainty about those expectations.

Cortisol and testosterone increase financial risk taking and may destabilize markets.

It is widely known that financial markets can become dangerously unstable, yet it is unclear why. Recent research has highlighted the possibility that endogenous hormones, in particular testosterone and cortisol, may critically influence traders’ financial decision making. Here we show that cortisol, a hormone that modulates the response to physical or psychological stress, predicts instability in financial markets. Specifically, we recorded salivary levels of cortisol and testosterone in people participating in an experimental asset market (N = 142) and found that individual and aggregate levels of endogenous cortisol predict subsequent risk-taking and price instability. We then administered either cortisol (single oral dose of 100 mg hydrocortisone, N = 34) or testosterone (three doses of 10 g transdermal 1% testosterone gel over 48 hours, N = 41) to young males before they played an asset trading game. We found that both cortisol and testosterone shifted investment towards riskier assets. Cortisol appears to affect risk preferences directly, whereas testosterone operates by inducing increased optimism about future price changes. Our results suggest that changes in both cortisol and testosterone could play a destabilizing role in financial markets through increased risk taking behaviour, acting via different behavioural pathways.

Optimal Charging of Electric Vehicles using a Stochastic Dynamic Programming Model and Price Prediction

Copyright © 2015 SAE International. The idea of grid friendly charging is to use electricity from the grid to charge batteries when electricity is available in surplus and cheap. The goal is twofold: to avoid putting additional load on the electricity grid and to reduce the cost to the consumer. To achieve this, a smart meter and a tariff with variable electricity prices has to be in place. In Day Ahead tariff (DA), prices are announced in advance for the next day, and this information can be used to select the cheapest times to charge the battery by the required amount. The optimization method is very simple, and it only has to be run once per day. However, the balance of supply and demand is not fully known in advance. Therefore Real Time Pricing (RTP) tariff supplies electricity at spot market rate depending on the current balance. This makes the charging process less predictable because it adds a stochastic element, but it does offer the potential of higher savings if future prices can be predicted with a reasonable degree of accuracy. This paper proposes an optimal controller based on a stochastic dynamic program (SDP), which predicts future price changes from available data. The controller takes into account price variability via a simple grid model that allows of unexpected price rises and a gradual return to a normal grid price. The DP algorithm has two variables, the state of charge (SoC) and the current electricity cost. It traces the expected total cost based on the stochastic model and makes a decision ‘to charge or not’ to minimize the expected (average) total cost. The results show that in case of a positive probability of price rises, the time to charge is chosen slightly before the lowest expected cost during the night. This is a rational solution, because waiting longer does increase the risk of an unexpected price spike. In the trivial case of a zero probability of unexpected price rises, the solution converges to the one found by the previous deterministic optimization algorithm.

The Price Formation of Raw Wood in the Czech Republic and a Comparison with the Neighbor States

Abstract The paper deals with the price development and pricing models in the Czech Republic and the neighbour states (Austria and Germany). The price of wooden material is a complicated indicator in methodical terms. In each country different factors influence the price development. The price development of raw wood in the Czech Republic is extraordinary in many aspects. The position of wood producers and companies in the wood processing industry in the market influences the overall position of global economy and the impact of negative factors following from the development of global economy in the previous years. The price issues of individual ranges of raw wood and their development come under the market mechanism and its relation of demand and offer. The material for the analysis of pricing models was obtained with secondary research based on getting information from the publicly available scientific and specialised literature, domestic and foreign internet sources, information from statistical databases and consultations with experts from practice. The employed methods were the analysis, comparison, consolidation and synthesis. Gained results were processed with fundamental statistical methods (elementary characteristics of timelines, correlation and the significance test of correlation coefficient). With the gained results theoretical possibilities of future price changes in contract relations were formulated in the conditions of the Czech Republic.

Measuring Expectations from Household Surveys: New Results on Subjective Probabilities of Future House Prices

I analyse new data on subjective probabilistic expectations on house prices collected in the Spanish Survey of Household Finances. Households are asked to distribute ten points among five different scenarios for the change in the price of their homes over the next 12 months. This paper is the first empirical study to document the beliefs of a representative sample of households about the future value of their homes. It also reviews the methodology of expectation measurement and recent work on household subjective probabilities. I model individual subjective probability densities using splines, construct quantiles from those densities, and analyse how the heterogeneity in the individual distributions relates to differences in housing and household characteristics. An important result of the paper is that women are more optimistic about the evolution of house prices than men. Location at the postal code level accounts for a large fraction of the variation in the subjective distributions across households. Finally, I provide some results on how subjective expectations matter for predicting spending behaviour. Housing investment and car purchases are negatively associated with pessimistic expectations about future house price changes and with uncertainty about those expectations.

Fundamentals or trends? A long-term perspective on house prices

Using a long-term time series covering 350 years of house prices along the Herengracht in Amsterdam, we examine whether a fundamental factor or a trend explains house prices and whether their explanatory power is time varying. We find that agents in the housing market switch in their formation of expectations about future changes in house prices between fundamental and momentum strategies. Specifically, we show that agents base their expectations more on fundamentals during economic slowdowns and more on recent trends or momentum during economic booms.

Energy futures prices and commodity index investment: New evidence from firm-level position data

This study brings fresh data to the highly-charged debate about the price impact of long-only index investment in energy futures markets. We use high frequency daily position data for NYMEX crude oil, heating oil, RBOB gasoline, and natural gas that are available from a representative large commodity index fund (“the Fund”) from February 13, 2007 through May 30, 2012. Simple correlation tests, difference-in-means tests, and Granger causality tests generally fail to reject the null hypothesis that changes in Fund positions are unrelated to subsequent returns in all four energy futures markets. We also fail to find any evidence that Fund positions are related to price movements in the WTI crude oil futures market using Singleton's (2014) long-horizon regression specification. Our results suggest Singleton's original finding of significant impacts and high levels of predictability may be simply an artifact of the method used to impute crude oil positions of index investors in a particular sample period. Overall, the empirical tests in this study fail to find compelling evidence of predictive links between commodity index investment and changes in energy futures prices.

X-CAPM: An extrapolative capital asset pricing model

Survey evidence suggests that many investors form beliefs about future stock market returns by extrapolating past returns. Such beliefs are hard to reconcile with existing models of the aggregate stock market. We study a consumption-based asset pricing model in which some investors form beliefs about future price changes in the stock market by extrapolating past price changes, while other investors hold fully rational beliefs. We find that the model captures many features of actual prices and returns; importantly, however, it is also consistent with the survey evidence on investor expectations.

Irrational exuberance and neural crash warning signals during endogenous experimental market bubbles.

Groups of humans routinely misassign value to complex future events, especially in settings involving the exchange of resources. If properly structured, experimental markets can act as excellent probes of human group-level valuation mechanisms during pathological overvaluations--price bubbles. The connection between the behavioral and neural underpinnings of such phenomena has been absent, in part due to a lack of enabling technology. We used a multisubject functional MRI paradigm to measure neural activity in human subjects participating in experimental asset markets in which endogenous price bubbles formed and crashed. Although many ideas exist about how and why such bubbles may form and how to identify them, our experiment provided a window on the connection between neural responses and behavioral acts (buying and selling) that created the bubbles. We show that aggregate neural activity in the nucleus accumbens (NAcc) tracks the price bubble and that NAcc activity aggregated within a market predicts future price changes and crashes. Furthermore, the lowest-earning subjects express a stronger tendency to buy as a function of measured NAcc activity. Conversely, we report a signal in the anterior insular cortex in the highest earners that precedes the impending price peak, is associated with a higher propensity to sell in high earners, and that may represent a neural early warning signal in these subjects. Such markets could be a model system to understand neural and behavior mechanisms in other settings where emergent group-level activity exhibits mistaken belief or valuation.