Essays on Financial Analyst Forecasts and Recommendations

The trade of the financial analyst is currently a much-debated issue in today’s media. As a large part of the investment analysis is conducted under the broker firms’ regime, the incentives of the financial analyst and the investor do not always align. The broker firm’s commercial incentives may be to maximise its commission from securities trading and underwriting fees. The purpose of this thesis is to extend our understanding of the work of a financial analyst, the incentives he faces and how these affect his actions. The first essay investigates how the economic significance of the coverage of a particular firm impacts the analysts’ accuracy of estimation. The hypothesis is that analysts put more effort in analysing firms with a relatively higher trading volume, as these firms usually yield higher commissions. The second essay investigates how analysts interpret new financial statement information. The essay shows that analysts underreact or overreact to prior reported earnings, depending on the short-term pattern in reported earnings. The third essay investigates the possible investment value in Finnish stock recommendations, issued by sell side analysts. It is established that consensus recommendations issued on Finnish stocks contain investment value. Further, the investment value in consensus recommendations improves significantly through the exclusion of recommendations issued by banks. The fourth essay investigates investors’ behaviour prior to financial analysts’ earnings forecast revisions. Lately, the financial press have reported cases were financial analysts warn their preferred clients of possible earnings forecast revisions. However, in the light of the empirical results, it appears that the problem of analysts leaking information to some selected customers does not appear systematically on the Finnish stock market.

Three Essays on the Volatility of Finnish Stock Returns (summary section only)

First, in Essay 1, we test whether it is possible to forecast Finnish Options Index return volatility by examining the out-of-sample predictive ability of several common volatility models with alternative well-known methods; and find additional evidence for the predictability of volatility and for the superiority of the more complicated models over the simpler ones. Secondly, in Essay 2, the aggregated volatility of stocks listed on the Helsinki Stock Exchange is decomposed into a market, industry-and firm-level component, and it is found that firm-level (i.e., idiosyncratic) volatility has increased in time, is more substantial than the two former, predicts GDP growth, moves countercyclically and as well as the other components is persistent. Thirdly, in Essay 3, we are among the first in the literature to seek for firm-specific determinants of idiosyncratic volatility in a multivariate setting, and find for the cross-section of stocks listed on the Helsinki Stock Exchange that industrial focus, trading volume, and block ownership, are positively associated with idiosyncratic volatility estimates––obtained from both the CAPM and the Fama and French three-factor model with local and international benchmark portfolios––whereas a negative relation holds between firm age as well as size and idiosyncratic volatility.

Modeling Nonlinearities and Asymmetries in Asset Pricing

Financial time series tend to behave in a manner that is not directly drawn from a normal distribution. Asymmetries and nonlinearities are usually seen and these characteristics need to be taken into account. To make forecasts and predictions of future return and risk is rather complicated. The existing models for predicting risk are of help to a certain degree, but the complexity in financial time series data makes it difficult. The introduction of nonlinearities and asymmetries for the purpose of better models and forecasts regarding both mean and variance is supported by the essays in this dissertation. Linear and nonlinear models are consequently introduced in this dissertation. The advantages of nonlinear models are that they can take into account asymmetries. Asymmetric patterns usually mean that large negative returns appear more often than positive returns of the same magnitude. This goes hand in hand with the fact that negative returns are associated with higher risk than in the case where positive returns of the same magnitude are observed. The reason why these models are of high importance lies in the ability to make the best possible estimations and predictions of future returns and for predicting risk.

Short-term Price Behavior and the Effect of Foreign Investors in Finnish Equity Markets (summary section only)

As globalization and capital free movement has increased, so has interest in the effects of that global money flow, especially during financial crises. The concern has been that large global money flows will affect the pricing of small local markets by causing, in particular, overreaction. The purpose of this thesis is to contribute to the body of work concerning short-term under- and overreaction and the short-term effects of foreign investment flow in the small Finnish equity markets. This thesis also compares foreign execution return to domestic execution return. This study’s results indicate that short-term under- and overreaction occurs in domestic-buy portfolios (domestic net buying) rather than in foreign-buy portfolios. This under- and overreaction, however, is not economically meaningful after controlling for the bid-ask bounce effect. Based on this finding, one can conclude that foreign investors do not have a destabilizing effect in the short-term in the Finnish markets. Foreign activity affects short-term returns. When foreign investors are net buyers (sellers) there are positive (negative) market adjusted returns. Literature related to nationality and institutional effect leads us to expect these kind of results. These foreign flows are persistent at a 5 % to 21 % level and the persistence of foreign buy flow is higher than the foreign sell flow. Foreign daily trading execution is worse than domestic execution. Literature which quantifies foreign investors as liquidity demanders and literature related to front-running leads us to expect poorer foreign execution than domestic execution.

The expiration day effect of indexoptions and index futures on the underlying shares

This paper studies the effect of the expiration day of index options and futures on the trading volume, variance and price of the underlying shares. The data consists of all trades for the underlying shares in the FOX-index for expiration days during the period October 1995 to the mid of yer 1999. The main results seem to support the findings of Kan 2001, i.e. no manipulation on a larger scale. However, some indication of manipulation could be found if certain characteristics are favorable. These characteristics include: a) a large quantity of outstanding futures or at/in the money options contracts, b) there exists shares with high index weight but fairly low trading volume. Lastly, there is some indication that manipulation might be more popular towards the end of the examined time period.

Hedging Options with Different Time Units in the Pricing Models

This study examined the effects of the Greeks of the options and the trading results of delta hedging strategies, with three different time units or option-pricing models. These time units were calendar time, trading time and continuous time using discrete approximation (CTDA) time. The CTDA time model is a pricing model, that among others accounts for intraday and weekend, patterns in volatility. For the CTDA time model some additional theta measures, which were believed to be usable in trading, were developed. The study appears to verify that there were differences in the Greeks with different time units. It also revealed that these differences influence the delta hedging of options or portfolios. Although it is difficult to say anything about which is the most usable of the different time models, as this much depends on the traders view of the passing of time, different market conditions and different portfolios, the CTDA time model can be viewed as an attractive alternative.

The Day of the Week Effect and Option Pricing - A Study of the German Option Market

The use of different time units in option pricing may lead to inconsistent estimates of time decay and spurious jumps in implied volatilities. Different time units in the pricing model leads to different implied volatilities although the option price itself is the same.The chosen time unit should make it necessary to adjust the volatility parameter only when there are some fundamental reasons for it and not due to wrong specifications of the model. This paper examined the effects of option pricing using different time hypotheses and empirically investigated which time frame the option markets in Germany employ over weekdays. The paper specifically tries to get a picture of how the market prices options. The results seem to verify that the German market behaves in a fashion that deviates from the most traditional time units in option pricing, calendar and trading days. The study also showed that the implied volatility of Thursdays was somewhat higher and thus differed from the pattern of other days of the week. Using a GARCH model to further investigate the effect showed that although a traditional tests, like the analysis of variance, indicated a negative return for Thursday during the same period as the implied volatilities used, this was not supported using a GARCH model.

Evaluating Option Pricing Models

This study evaluates three different time units in option pricing: trading time, calendar time and continuous time using discrete approximations (CTDA). The CTDA-time model partitions the trading day into 30-minute intervals, where each interval is given a weight corresponding to the historical volatility in the respective interval. Furthermore, the non-trading volatility, both overnight and weekend volatility, is included in the first interval of the trading day in the CTDA model. The three models are tested on market prices. The results indicate that the trading-time model gives the best fit to market prices in line with the results of previous studies, but contrary to expectations under non-arbitrage option pricing. Under non-arbitrage pricing, the option premium should reflect the cost of hedging the expected volatility during the option’s remaining life. The study concludes that the historical patterns in volatility are not fully accounted for by the market, rather the market prices options closer to trading time.

Test of the Clustering Hypothesis in the Helsinki Exchanges

This paper investigates the clustering pattern in the Finnish stock market. Using trading volume and time as factors capturing the clustering pattern in the market, the Keim and Madhavan (1996) and the Engle and Russell (1998) model provide the framework for the analysis. The descriptive and the parametric analysis provide evidences that an important determinant of the famous U-shape pattern in the market is the rate of information arrivals as measured by large trading volumes and durations at the market open and close. Precisely, 1) the larger the trading volume, the greater the impact on prices both in the short and the long run, thus prices will differ across quantities. 2) Large trading volume is a non-linear function of price changes in the long run. 3) Arrival times are positively autocorrelated, indicating a clustering pattern and 4) Information arrivals as approximated by durations are negatively related to trading flow.

Do Analysts Leak Information to Preferred Customers?

Inspired by the recent debate in the financial press, we set out to investigate if financial analysts warn their preferred customers of possible earnings forecast revisions. The issue is explored by monitoring investors’ trading behavior during the weeks prior to analyst earnings forecast revisions, using the unique official stock transactions data set from Finland. In summary, we do not find evidence of large investors systematically being warned of earnings forecast revisions. However, the results indicate that the very largest investors show trading behavior partly consistent with being informed of future earnings forecast revisions.

Intraday and Weekend Volatility Patterns- Implications for Option Pricing

This study examines the intraday and weekend volatility on the German DAX. The intraday volatility is partitioned into smaller intervals and compared to a whole day’s volatility. The estimated intraday variance is U-shaped and the weekend variance is estimated to 19 % of a normal trading day. The patterns in the intraday and weekend volatility are used to develop an extension to the Black and Scholes formula to form a new time basis. Calendar or trading days are commonly used for measuring time in option pricing. The Continuous Time using Discrete Approximations model (CTDA) developed in this study uses a measure of time with smaller intervals, approaching continuous time. The model presented accounts for the lapse of time during trading only. Arbitrage pricing suggests that the option price equals the expected cost of hedging volatility during the option’s remaining life. In this model, time is allowed to lapse as volatility occurs on an intraday basis. The measure of time is modified in CTDA to correct for the non-constant volatility and to account for the patterns in volatility.

The Hybrid Consumer: Exploring the Drivers of a New Consumer Behaviour Type

A dramatically different consumption pattern seems to be emerging among a vast group of consumers. This may mean that conventional consumer stereotypes and segmentation theory are becoming outdated. The so called hybrid consumers seem to increasingly opt for both premium and budget alternatives in various product and service categories while mid-priced alternatives are losing share in their consumption basket. Although this type of polarisation, or dispersion, is already recognized as an important change, hybrid behaviour is still largely under-researched. The present study aims to analyze the possible drivers of hybrid consumption and by identifying typical categories and situations of trading up versus trading down derive tentative characteristics of hybrid consumption for further research on the topic. A tentative pattern of hybrid consumption was identified, which relates trading up to high-involvement, discretional spending and trading down to low-involvement necessities. However, it was also found that hybrid consumption transcends product category boundaries and may thus be less straightforward than previously perhaps assumed. In addition, a purchase pattern continuum was developed, accounting for various degrees of hybrid consumption.

International Portfolio Investments and The Informational Value of Trade

A large volume of literature suggests that information asymmetry resulting from the spatial separation between investors and investments have a significant impact on the composition of investors’ domestic and international portfolios. I show that institutional factors affecting trading in tangible goods help explain a substantial portion of investors’ spatial bias. More importantly, I demonstrate that an information flow medium with breadth and richness directly linked to the bilateral commitment of resources between countries, that I measure by their trading intensity in tangible goods, is consistent with the prevailing country allocation in investors’ international portfolios.

A modell of socially optimal hydro reservoir usage - yelding socially optimal outcomes in nordic electricity markets

An extensive electricity transmission network facilitates electricity trading between Finland, Sweden, Norway and Denmark. Currently most of the area's power generation is traded at NordPool, where the trading volumes have steadily increased since the early 1990's, when the exchange was founded. The Nordic electricity is expected to follow the current trend and further integrate with the other European electricity markets. Hydro power is the source for roughly a half of the supply in the Nordic electricity market and most of the hydro is generated in Norway. The dominating role of hydro power distinguishes the Nordic electricity market from most of the other market places. Production of hydro power varies mainly due to hydro reservoirs and demand for electricity. Hydro reservoirs are affected by water inflows that differ each year. The hydro reservoirs explain remarkably the behaviour of the Nordic electricity markets. Therefore among others, Kauppi and Liski (2008) have developed a model that analyzes the behaviour of the markets using hydro reservoirs as explanatory factors. Their model includes, for example, welfare loss due to socially suboptimal hydro reservoir usage, socially optimal electricity price, hydro reservoir storage and thermal reservoir storage; that are referred as outcomes. However, the model does not explain the real market condition but rather an ideal situation. In the model the market is controlled by one agent, i.e. one agent controls all the power generation reserves; it is referred to as a socially optimal strategy. Article by Kauppi and Liski (2008) includes an assumption where an individual agent has a certain fraction of market power, e.g. 20 % or 30 %. In order to maintain the focus of this thesis, this part of their paper is omitted. The goal of this thesis is two-fold. Firstly we expand the results from the socially optimal strategy for years 2006-08, as the earlier study finishes in 2005. The second objective is to improve on the methods from the previous study. This thesis results several outcomes (SPOT-price and welfare loss, etc.) due to socially optimal actions. Welfare loss is interesting as it describes the inefficiency of the market. SPOT-price is an important output for the market participants as it often has an effect on end users' electricity bills. Another function is to modify and try to improve the model by means of using more accurate input data, e.g. by considering pollution trade rights effect on input data. After modifications to the model, new welfare losses are calculated and compared with the same results before the modifications. The hydro reservoir has the higher explanatory significance in the model followed by thermal power. In Nordic markets, thermal power reserves are mostly nuclear power and other thermal sources (coal, natural gas, oil, peat). It can be argued that hydro and thermal reservoirs determine electricity supply. Roughly speaking, the model takes into account electricity demand and supply, and several parameters related to them (water inflow, oil price, etc.), yielding finally the socially optimal outcomes. The author of this thesis is not aware of any similar model being tested before. There have been some other studies that are close to the Kauppi and Liski (2008) model, but those have a somewhat different focus. For example, a specific feature in the model is the focus on long-run capacity usage that differs from the previous studies on short-run market power. The closest study to the model is from California's wholesale electricity markets that, however, uses different methodology. Work is constructed as follows.

Algorithms for Exact Structure Discovery in Bayesian Networks

Bayesian networks are compact, flexible, and interpretable representations of a joint distribution. When the network structure is unknown but there are observational data at hand, one can try to learn the network structure. This is called structure discovery. This thesis contributes to two areas of structure discovery in Bayesian networks: space--time tradeoffs and learning ancestor relations. The fastest exact algorithms for structure discovery in Bayesian networks are based on dynamic programming and use excessive amounts of space. Motivated by the space usage, several schemes for trading space against time are presented. These schemes are presented in a general setting for a class of computational problems called permutation problems; structure discovery in Bayesian networks is seen as a challenging variant of the permutation problems. The main contribution in the area of the space--time tradeoffs is the partial order approach, in which the standard dynamic programming algorithm is extended to run over partial orders. In particular, a certain family of partial orders called parallel bucket orders is considered. A partial order scheme that provably yields an optimal space--time tradeoff within parallel bucket orders is presented. Also practical issues concerning parallel bucket orders are discussed. Learning ancestor relations, that is, directed paths between nodes, is motivated by the need for robust summaries of the network structures when there are unobserved nodes at work. Ancestor relations are nonmodular features and hence learning them is more difficult than modular features. A dynamic programming algorithm is presented for computing posterior probabilities of ancestor relations exactly. Empirical tests suggest that ancestor relations can be learned from observational data almost as accurately as arcs even in the presence of unobserved nodes.