Achieving Amicable Settlements and Possible Reconciliations : The Role of Forensic Accountants in Equitable Distributions

This book is focussed on investigating how a proper implementation of forensic accounting tools could serve as a means and channel whereby such techniques as valuations, equitable distribution and evidence could be employed in avoiding unnecessary break ups and emotional breakdowns. Through the exploration of options which are available to marital couples considering separation or divorce during periods of crises, the book aims to emphasise the theme that a break from the relationship may be the step required to avert a break-up. The role of forensic accounting in facilitating an amicable process during such a break - which could result in the possible restoration of relationships involved during such crucial stage also constitutes a recurring theme of the book. It is a well known fact that financial problems constitute the source of break-downs in many relationships. Whilst other factors may contribute to failures in relationships and whilst some couples may have finalised their intentions and require very little assistance in getting through such painstaking processes, others may have their decisions influenced by court procedures, counselling sessions and the proper application of equitable distribution procedures – such equitable distribution procedure being considered a preferred technique in resolving marital asset distributions than the community property concept. Further this book highlights factors which need to be taken into consideration – not only in averting unnecessary break-ups, but also in facilitating harmonious and amicable settlements which may eventually pave the way for reconciliation, as well as restoration of broken down relationships. Whilst planning of marital asset distribution should not constitute the focus of any marriage, planning when the need arises may serve not only as a channel whereby a relationship can be restored eventually, but as a temporary means of weathering the storms during the difficult times in the relationship

What Factors Drive Analyst Forecasts?

A firm's competitive environment, its strategic choices, and its internal capabilities are considered important determinants of its future performance. Yet there is little evidence on whether analysts' forecasts of firm performance actually reflect any of these factors and which are considered most important. We use survey data from 967 analysts ranking 837 companies to judge how their forecasts are related to evaluations of firms' industry competitiveness, strategic choices, and internal capabilities. Forecasts are generally associated with many of the factors that money managers rate as important in their assessments of analyst contributions, including industry growth and competitiveness, low-price strategy, strategy execution, top management quality, innovation, and performance-driven culture. We also find wide variation across variables for ratings consistency among analysts covering the same firm. On average, consistency is higher for sell-side than buy-side analysts, consistent with sell-side analysts facing greater incentives to herd

Herding and Anchoring in Macroeconomic Forecasts: The Case of the PMI

We test if analysts display multiple biases in forecasting the Institute for Supply Management’s (ISM) manufacturing Purchasing Manager’s Index (PMI). We adopt a test that does not require knowledge of the forecaster’s prior information set and is robust to rational clustering, correlated forecast errors and outliers. We find that analysts forecast the PMI poorly and display multiple biases when forecasting. In particular, forecasters anti-herd and anti-anchor. Anti-herding supports a reputation-based notion that forecasters are rewarded not only for forecast accuracy but also for being the best forecast at a single point in time. Anti-anchoring is consistent with forecasters overreacting to private information. The two biases show a strong positive correlation suggesting that the incentives that elicit anti-herding also elicit anti-anchoring behavior. Both biases result in larger absolute errors, although the effect is stronger for anti-herding

A More Efficient and Effective Objective Measure of Financial Disclosure Quality: Omissions of Seven Key Financial Statement Variables

This dissertation research includes three Chapters. Chapter One proposes a new and simple measure of financial reporting quality. Chapter Two and Chapter Three apply this new measure to examine the association between financial reporting quality and firms\u27 internal governance issues, such as internal control quality and a possible outcome of internal control weakness (ICW), financial restatements.In Chapter One of the thesis, I propose a parsimonious, theory-based and empirically-supported measure of missing variables, REPORT. Chen et al. (2015) proposes a measure of disclosure quality, DQ, based on missing financial statement variables. DQ includes hundreds of items and is complex to program. I propose a parsimonious, theory-based and empirically-supported measure of missing variables, REPORT, based on the seven variables found most value relevant by Lev and Thiagarajan (1993). To compare REPORT with DQ, I replicate Chen, et al (2015) and find results as originally reported. REPORT also is similarly associated with the measures used to validate DQ, forecast accuracy, analyst forecast dispersion, bid-ask spread, and cost of capital. With Vuong and Clarke tests I compare REPORT and DQ with these disclosure quality metrics and find that REPORT performs as well as, or better than, DQ in these tests. The comparative power of REPORT over DQ indicates that omission of the elements of a small set of highly value-relevant financial variables better indicates a firm\u27s disclosure quality than omissions of a larger set of variables that also includes less value-relevant, or irrelevant, financial variables. REPORT, being theory-based, omits examination of many items likely unnecessary to firm valuation and is easily implemented, not only by trained researchers, but even by average investors, which opens many potential applications in both academic and practical areas. Chapter Two examines the association between internal control weakness (ICW) and the two missing data -based measures, DQ and REPORT. Being able to identify traits of firms of ICWs before their public issuances would provide investors with more information to plan for their investment decisions. As an important aspect of internal control, financial statement preparation quality may reflect firms\u27 internal control system. Poor internal control system may cause omissions of numbers reported on firms\u27 financial statements. I examine whether reporting or omitting of financial statement variables can reflect firms\u27 internal control quality; I also examine if omitting financial statement variable is informative in predicting issuance of an ICW in the next period. However, the results lack adequate statistical significance in drawing conclusions in terms of associations between ICWs and DQ/REPORT. Chapter Three examines the association between misstatements and DQ/REPORT. Internal control weakness and restatements do not always coincide. Internal control weaknesses, alone, cannot fully reflect firms\u27 risks of restatements. In this study, I examine whether DQ and REPORT provides with additional information, other than ICWs, in explaining likelihood of misstatements. I expect that firms with higher DQ and REPORT are less likely to restate their financial statements in subsequent periods. The results lack adequate evidence to draw conclusions on associations between restatements and DQ/REPORT, but the results have added further evidence to the literature on associations between ICWs and likelihood of misstatements

Relatedness, National Boarders, Perceptions of Firms and the Value of Their innovations

The main goal of this dissertation is to better understand how external corporate stakeholder perceptions of relatedness affect important outcomes for companies. In pursuit of this goal, I apply the lens of category studies. Categories not only help audiences to distinguish between members of different categories, they also convey patterns of relatedness. In turn, this may have implications for understanding how audiences search, what they attend to, and how the members are ultimately valued. In the first chapter, I apply incites from social psychology to show how the nationality of audience members affects the way that they cognitively group objects into similar categories. I find that the geographic location of stock market analysts affect the degree to which they will revise their earnings estimates for a given company in the wake of an earnings miss by another firm in the same industry. Foreign analysts revise their earnings estimates downward more so than do local analysts, suggesting that foreign analysts ascribe the earnings miss more broadly and tend to lump companies located in the same country into larger groups than do local analysts. In the second chapter, I demonstrate that the structure of inter-category relationships can have consequential effects for the members of a focal category. Leveraging an experimental-like design, I study the outcomes of nanotechnology patents and the pattern of forward citations across multiple patent jurisdictions. I find that members of technology categories with many close category \u27neighbors\u27 are more broadly cited than members of categories with few category \u27neighbors.’ My findings highlight how category embeddedness and category system structure affect the outcomes of category members as well as the role that classification plays in the valuation of innovation. In the third chapter, I propose a novel and dynamic measure of corporate similarity that is constructed from the two-mode analyst and company coverage network. The approach creates a fine-grained continuous measure of company similarity that can be used as an alternative or supplement to existing static industry classification systems. I demonstrate the value of this new measure in the context of predicting financial market responses to merger and acquisition deals

Estimating Demand Uncertainty Using Judgmental Forecasts

Measuring demand uncertainty is a key activity in supply chain planning. Of various methods of estimating the standard deviation of demand, one that has been employed successfully in the recent literature uses dispersion among expertsâ forecasts. However, there has been limited empirical validation of this methodology. In this paper we provide a general methodology for estimating the standard deviation of a random variable using dispersion among expertsâ forecasts. We test this methodology using three datasets, demand data at item level, sales data at firm level for retailers, and sales data at firm level for manufacturers. We show that the standard deviation of a random variable (demand and sales for our datasets) is positively correlated with dispersion among expertsâ forecasts. Further we use longitudinal datasets with sales forecasts made 3-9 months before earnings report date for retailers and manufacturers to show that the effects of dispersion and scale on standard deviation of forecast error are consistent over time.Operations Management Working Papers Serie