NASDAQ OMX Research Articles

Consentaneous agent-based and stochastic model of the financial markets.

We are looking for the agent-based treatment of the financial markets considering necessity to build bridges between microscopic, agent based, and macroscopic, phenomenological modeling. The acknowledgment that agent-based modeling framework, which may provide qualitative and quantitative understanding of the financial markets, is very ambiguous emphasizes the exceptional value of well defined analytically tractable agent systems. Herding as one of the behavior peculiarities considered in the behavioral finance is the main property of the agent interactions we deal with in this contribution. Looking for the consentaneous agent-based and macroscopic approach we combine two origins of the noise: exogenous one, related to the information flow, and endogenous one, arising form the complex stochastic dynamics of agents. As a result we propose a three state agent-based herding model of the financial markets. From this agent-based model we derive a set of stochastic differential equations, which describes underlying macroscopic dynamics of agent population and log price in the financial markets. The obtained solution is then subjected to the exogenous noise, which shapes instantaneous return fluctuations. We test both Gaussian and q-Gaussian noise as a source of the short term fluctuations. The resulting model of the return in the financial markets with the same set of parameters reproduces empirical probability and spectral densities of absolute return observed in New York, Warsaw and NASDAQ OMX Vilnius Stock Exchanges. Our result confirms the prevalent idea in behavioral finance that herding interactions may be dominant over agent rationality and contribute towards bubble formation.

Activity evaluation of Lithuania credit unions

The bank of Lithuania in its supervisory functions evaluates how credit unions carries out the prudential norms (capital adequacy, liquidity, maximum open position in foreign currency, the maximum loan amount per borrower, large loan requirements), examine the overall financial condition of the credit union, is watching whether the credit union's activities comply with laws, regulations, statutes and internal union document requirements. However, the steady growth of the credit union's capital, loan number and size of credit unions operating regulations limiting still does not measure the credit union's business risk profile and operational efficiency, protect against failures, errors or loss. The analysis of scientific literature also shows that there is no unanimous and undisputed methodology for enterprises performance evaluation. Moreover, there is no company's evaluation models adapted to specifically interested persons group and one to of the financial institutions, i.e. credit unions. Therefore, performance evaluation of credit union and prediction research of their financial parameters in this area is valuable, relevant and new, in both theoretical and practical terms. The aim - to perform Lithuanian credit unions comparative performance evaluation. The scientific literature suggests that credit unions' assessment can be carried out both traditional (dynamics, structure, relative indicators, business continuity) and the innovative modern methods. It should be emphasized that the evaluation of the activities is individual for each business enterprise, because it depends on many factors, such as: object of assessment, the assessment purpose; the assessment period; assessment of the required information and the availability of data, the evaluation period, the users of evaluation results and other. It should be noted that the main users of credit unions performance assessment results are the owners (partners), creditors and future owners and lenders and borrowers. According to the Securities stock exchange NASDAQ OMX Vilnius (2010) proposed financial ratios calculation methodology were calculated and compared 76 credit unions operating in Lithuania profitability, efficiency, and financial leverage ratios. By using linear and exponential trend methodology analysed the mean trends of Lithuanian credit unions key performance indicators. It should be noted that setup of complex financial indicators, tailored specifically to the financial institution with the opportunity to compare it with other relevant authorities of the credit unions' assessment and performance expected.

Accurate medium-term wind power forecasting in a censored classification framework

We provide a wind power forecasting methodology that exploits many of the actual data's statistical features, in particular both-sided censoring. While other tools ignore many of the important “stylized facts” or provide forecasts for short-term horizons only, our approach focuses on medium-term forecasts, which are especially necessary for practitioners in the forward electricity markets of many power trading places; for example, NASDAQ OMX Commodities (formerly Nord Pool OMX Commodities) in northern Europe. We show that our model produces turbine-specific forecasts that are significantly more accurate in comparison to established benchmark models and present an application that illustrates the financial impact of more accurate forecasts obtained using our methodology.

Systematic trading behaviour and its informational effect: evidence from the OMXH

By using signed small trade turnover (SSTT) as a proxy of investors’ systematic trading behaviour and permanent price effect (PPE) as a proxy for informed trading, this article investigates the short-term outperformance of high SSTT stocks for categories of investors on the NASDAQ OMX Helsinki (OMXH) and whether the outperformance is best understood in the context of investor trading behaviour or informed trading. This study finds that the trading of high SSTT stocks is associated with a lower PPE than the trading of low SSTT stocks, which implies that the trading of high SSTT stocks is less informed than the trading of low SSTT stocks, and that the short-term outperformance of high SSTT stocks relative to low SSTT stocks is best explained within the behavioural finance framework, that is, by investors’ bias towards popular stocks proxied by SSTT, and not within the rational framework, that is, by informed trading proxied by the PPE. These findings are economically and statistically significant for all ty...

Audit Partner Public-Client Specialisation and Client Abnormal Accruals

We examine the association of Big 4 audit partners’ public-client specialisation with client companies’ audit quality. Using a sample of NASDAQ OMX companies in Finland, we identify the audit partner assigned to each public-client engagement. We expect that partners with greater public-client specialisation provide higher quality auditing, since they have likely developed deep domain-specific knowledge and a keen sense of the litigation and reputational risks posed by public clients. In addition, the willingness to resist client pressure likely increases with the number of public clients in the partner's portfolio because dependence on any one client diminishes, which should help to ensure audit quality. The results show that public-client specialisation is negatively associated with abnormal accruals, and this result is attributable to audit partners with three to six public clients. The results of supplemental tests imply that public-client specialisation is more important when general auditing experience is lower. Further, the results reveal that in our setting of high-tax and high alignment between financial reporting and tax reporting, greater public-client specialisation is particularly associated with smaller income-decreasing abnormal accruals, suggesting that auditors with greater public-client specialisation likely recognise the downside reputational implications and achieve audit quality by discouraging tax avoidance.

A Power Market Forward Curve with Hydrology Dependence - An Approach Based on Artificial Neural Networks

This paper develops an hourly forward curve for power markets where the intra-day and intra-week shapes (profiles) depend on the level of the hydrological balance. The shaping model is based on a feed-forward Artificial Neural Network (ANN), which is trained on a historical data set of hourly electricity spot prices from the Nord Pool market and weekly measurements of the Nordic hydrological balance. The yearly seasonal cycle is estimated with historical electricity forward prices from the Nasdaq OMX Commodities exchange. We calibrate the shaping model to prevailing electricity forward prices and proceed to demonstrate its most important properties. By using comparative static analysis we particulary focus on the hydro dependence of the shapes. We conclude the paper with a real world valuation task. By combining our proposed forward curve with a simple Ornstein-Uhlenbeck process we price a strip of hourly call options on the electricity spot price under different hydrological scenarios.

The share is down 8% after the profit warning, is it time to buy?

Variables explaining the market response to a profit warning are of interest to both market participants and the managers of the firm. Several variables have been used in previous research to explain the market response to profit warnings with varying outcomes. This study uses a framework of surprise and risk to explain the market response and tests it on a sample of 474 profit warnings collected from Nasdaq OMX Nordic. The findings show that surprise and risk variables can be used to estimate the size of the market response to a profit warning.

Pricing electricity swaptions under a stochastic volatility term structure model

This paper suggests a stochastic volatility term structure model applied to the pricing of electricity swaptions in the Nordic power market traded at the Nasdaq OMX Commodities exchange. The volatility structure in the model is specified as a product of a time-dependent function, which handles the maturity effect, and a Cox-Ingersoll-Ross process for the stochastic volatility. We employ a Fourier-based approach to pricing electricity swaptions and perform an empirical analysis by calibrating the model to a data set consisting of more than 12 000 pairs of implied bid-ask volatilities, corresponding to swaption prices from the Nordic power market. We show that our model outperforms the lognormal benchmark both in- and out-of-sample.

Swedish and Dutch listed companies’ compliance with IAS 36 paragraph 134

This article investigates the extent to which companies listed on the Nasdaq OMX (NOMX) and the Euronext Amsterdam (EA), in their 2005 and 2008 annual reports, complied with the disclosure requirements in IAS 36 paragraph 134, as well as the factors that explain why some companies complied with the standard to a higher extent than did other ones. Swedish and Dutch listed companies are chosen as the accounting oversight system differs between the countries. The relationship between the dependent variable, that is, information disclosed in accordance with IAS 36 paragraph 134 in the annual reports in Swedish and Dutch listed companies, and the independent variables, that is, accounting oversight, auditing company, size, leverage, future prospects, industry and learning, is examined. The results reveal that Swedish companies listed on the NOMX were more compliant than their Dutch counterparts in 2005, possibly because of the (historically) weak Dutch institutional oversight system. The compliance level seems to have increased in both Swedish and Dutch companies over time, thus indicating learning. In 2008, there was no significant difference in compliance level between Sweden and the Netherlands, which suggests convergence. Size significantly affected the compliance level in Sweden only, and leverage affected the compliance level in the Netherlands only. Moreover, non-financial companies were more compliant in both countries. The independent variables auditing company and future prospects did not seem to have a significant effect on the compliance level.

Systematic Trading Behavior and the Cross-Section of Stock Returns on the OMXH*

Signed small trade turnover (SSTT) measures temporary uninformed buy or sell pressure that is initiated by small trades in the same direction. Using Nasdaq OMX Helsinki tick-by-tick trade data with known investor category, we confirm that SSTT is a robust proxy for uninformed trading. In the short term (1–3 months), stocks with a high proportion of signed small trades outperform, but in the medium (4–6 months) and long term (7–36 months), SSTT by individual investors has a negative correlation to stock returns, while SSTT by institutional investors and foreign nominees is not related to stock returns. Systematic trading behavior appears to better explain the excess return generated by the low SSTT portfolio relative to the high SSTT portfolio when compared to traditional risk factors in the CAPM and Fama-French models. In the aggregate, small trades are noise in the spirit of Kyle (1985) and Black (1986), but small trade behavior impacts the performance of individuals and not the performance of institutions. Large trades by both individuals and institutions perform better in the intermediate and long term, which lends some credibility to the practice of classifying larger trades as informed, but our results show that these trades are not always by institutions.

Market Data Dissemination Policies: What is Important?

Even though the market data has a vital importance for the price discovery and the risk management for the aggregators and algorithms as well as for exchanges as an additional income source, the economics of data dissemination is quite new. This first academic attempt to understand the market data policies compares approaches to market data in selected exchanges (namely NASDAQ OMX, London Stock Exchange-LSE, Deutsche Borse, BOVESPA, Hong Kong Exchanges and Clearing-HKEx, Russian Trading System-RTS, Warsaw Stock Exchange-RTS, SIX Swiss Stock Exchange) to address the important components. The results show that in order to improve the market data revenues exchanges should have standard data dissemination policies based on product and price discrimination incorporating various types of counting the data as well as separate strategic approaches to reference, non-display and derived data.

Selective hedging in hydro-based electricity companies

We analyze risk management trends in electricity commodity markets using the production and transaction data and written hedging policies of 12 Norwegian hydropower companies. The scope of our analysis is the hedging of physical electricity production using the power derivatives available at NASDAQ OMX Commodities. In their hedging policy, these companies either use a Cashflow at Risk (C-FaR) approach or a hedge ratio approach, or follow no explicitly stated approach. We find that the derivative cashflows constitute substantial profits for these companies. Furthermore, hedging contributes to reducing the C-FaR for 10 of the companies. These findings are surprising considering that we expect hedging to yield zero expected profit and to smooth the earnings. Overall, our findings reveal that a practice of incorporating market views in hedging decisions is widespread in the sample companies, as both sanctioned in their written hedging policy and as indicated by the substantial hedging profits.

Predicting Intraday Price Distributions at High Frequencies

We propose extensions to the continuous-time random walk (CTRW) framework so far mainly developed within the econophysics community. Using numerical methods, we extend the CTRW framework with more general marginal distributions than previously proposed. There are two main findings in this respect: First, modeling the returns with a Student-t distribution gives at least as good price distribution predictions as the other previously proposed distributions in this framework. Second, a mixed-Weibull waiting time distribution fits exceptionally well to our three-week long Nasdaq OMX data. When combined with standard financial econometric GARCH and ACD models and intraday seasonality filtering procedures new to the CTRW framework, our models deliver realistic intraday Value-at-Risk and Expected Shortfall predictions. Compared to the basic CTRW model, the total average performance improvement is typically around 70 percent. The effect of filtering out memory in waiting times is particularly noticeable: around 50 percent of the total performance improvement. Overall, our extensions and filtering methods make the CTRW framework a useful tool for intraday risk management (trading), especially at high frequencies of less than a minute or so.

Fools’ Gold

Fools’ Gold

Pricing Electricity Swaptions Under a Stochastic Volatility Term-Structure Model

This paper suggests a stochastic volatility term-structure model applied to the pricing of electricity swaptions in the Nordic power market traded at the Nasdaq OMX Commodities exchange. The volatility structure in the model is specified as a product of a time-dependent function that handles the maturity effect, and a Cox-Ingersoll-Ross process for the stochastic volatility. We employ a Fourier based approach to price electricity swaptions and perform an empirical analysis by calibrating the model to a data set consisting of more than 12000 pairs of implied bid-ask volatilities corresponding to swaption prices from the Nordic power market. We show that our model outperforms the log-normal benchmark both in-sample and out-of-sample

Growing role of hedge funds in the economy

The objective of this article is to document the profound and growing role of hedge funds in the economy. It is accomplished through identification and analysis of numerous activities normally associated with hedge funds that have become an integral part of capital market activities. Examples include the phenomenal growth of exchange traded funds (ETFs) and mutual funds that follow hedge fund strategies. The number of mutual funds that hedge, short sell and use leverage is increasing. Hedge funds are key suppliers of liquidity in the stock and US Treasury bond markets. Commercial banks are investing the mortgages they originate as opposed to selling them in secondary markets. Hedge funds make commercial loans and modify residential mortgages. They underwrite reinsurance policies. Hedge funds significantly influence corporate governance by playing key roles in acquisitions and corporate reorganizations. NASDAQ OMX recently began listing the alpha index options intended to allow investors to trade options on the relative performance of a stock or ETF as compared with SPY. Hedge funds invest in sovereign debt, negotiate good terms and reap the rewards. They launch philanthropic organizations. The legal and competitive landscape is evolving in such a manner that fosters hedge funds investment strategies for many years to come.

Need for Speed? Low Latency Trading and Adverse Selection

This paper investigates the impact of market-wide low latency trading technologies on informational asymmetries between traders. It develops a model with two types of high-frequency traders: market makers (HFT-M) and ”bandits” (HFT-B), who profit by trading on stale quotes. The HFTs endogenously decide on information acquisition. Competitive HFT market-makers face higher adverse selection risks when latency drops, as the conditional probability of a liquidity motivated trade decreases. In equilibrium, they will charge higher spreads to compensate for the additional risk. Lower market latencies also provide incentives for market makers to gather information. Informed market makers reduce expected losses on stale quotes while still attracting liquidity demand from uninformed traders. We find empirical support for the model implications: the adverse selection component of the bid-ask spread increases by 15% (0.35 bps) on NASDAQ OMX after introducing low-latency technology. The effect is stronger in more volatile securities.

Trading Fast and Slow: Colocation and Market Quality

We exploit an optional colocation upgrade at NASDAQ OMX Stockholm to assess how speed affects market liquidity. Liquidity improves for the overall market and even for noncolocated trading entities. We find that the upgrade is pursued mainly by participants who engage in market making. Those that upgrade use their enhanced speed to reduce their exposure to adverse selection and to relax their inventory constraints. In particular, the upgraded trading entities remain competitive at the best bid and offer even when their inventories are in their top decile. Our results suggest that increasing the speed of market-making participants benefit market liquidity.

Tracing of stock market long term trend by information efficiency measures

The goal of the article is to research financial market efficiency and to recognize major reversal points of long-term trend of stock market index, which could indicate forthcoming crisis or market raise periods. We suggest computational model of financial time series analysis, which combines several approaches of soft computing, including information efficiency evaluation methods (Shannon's entropy, Hurst exponent), neural networks and sensitivity analysis. The model aims to derive the aggregated measure for evaluating efficiency of the financial market and to find its interrelationships with the reversal of long-term trend. The radial basis function neural network was designed for forecasting moments of cardinal changes in stock market behavior, expressed by its entropy values derived from the symbolized time series of stock market index. The performance of neural network model is explored by applying sensitivity analysis and resulted in selecting smoothing parameters of the input variables. The experimental research investigates behavior of the long-term trend of the three emerging financial markets within Nasdaq OMX Baltic stock exchange. Introduction of information efficiency measures improves ability of the model to recognize the approaching reversal of long-term trend from temporary market “nervousness” and can be useful for calibrating stock trading strategy.

Reference Price Formation for Packaged Transactions

This paper provides a new approach to determine the relevance of a reference price when investors decide if a security is to be kept or sold. We propose that the observed reluctance to crystallise losses and propensity to realise gains, a behavioural bias dubbed the disposition effect, may be a result of the reference investors apply to determine if an investments is in profit or at loss. We examine a comprehensive data set comprised of all trading records for investors within the Nasdaq OMX Helsinki market over a recent seven year period. We find that the disposition effect is statistically and economically significant in the short run for a sample of active individual investors that purchase shares at one price in one package. It appears that there can be significant reduction in the extent of the disposition effect by simply reducing the significance of the purchase price by spreading buy transactions across several trades and consequently across different market prices [packaging].