A simple scheme for allocating capital in a foreign exchange proprietary trading firm

We present a model of capital allocation in a foreign exchange proprietary trading firm. The owner allocates capital to individual traders, who operate within strict risk limits. Traders specialize in individual currencies, but are given discretion over their choice of trading rule. The owner provides the simple formula that determines position sizes – a formula that does not require estimation of the firm-level covariance matrix. We provide supporting empirical evidence of excess risk-adjusted returns to the firm-level portfolio, and we discuss a modification of the model in which the owner dictates the choice of trading rule

Sources of inaction in household finance: evidence from the Danish mortgage markets

We build an empirical model to attribute delays in mortgage refinancing to psychological costs inhibiting refinancing until incentives are sufficiently strong; and behavior, potentially attributable to information-gathering costs, lowering the probability of household refinancing per unit time at any incentive. We estimate the model on administrative panel data from Denmark, where mortgage refinancing without cash-out is unconstrained. Middle-aged and wealthy households act as if they have high psychological refinancing costs; but older, poorer, and less-educated households refinance with lower probability irrespective of incentives, thereby achieving lower savings. We use the model to understand frictions in the mortgage channel of monetary policy transmission

The detection of fraud activities on the stock market through forward analysis methodology of financial discussion boards

Financial discussion boards (FDBs) have been widely used for a variety of financial knowledge exchange activities through the posting of comments on the FDBs. Popular public FDBs are prone to be used as a medium to spread false financial information due to having a larger group of audiences. Although online forums, in general, are usually integrated with anti-spam tools such as Akismet, moderation of posted contents heavily relies on human moderators. Unfortunately, popular FDBs attract many comments per day which realistically prevents human moderators from continuously monitoring and moderating possibly fraudulent contents. Such manual moderation can be extremely time-consuming. Moreover, due to the absence of useful tools, no relevant authorities are actively monitoring and handling potential financial crimes on FDBs. This paper presents a novel forward analysis methodology implemented in an Information Extraction (IE) prototype system named FDBs Miner (FDBM). This methodology aims to detect potentially illegal comments on FDBs while integrating share prices in the detection process as this helps to categorise the potentially illegal comments into different risk levels for investigation priority. The IE prototype system will first extract the public comments and per minute share prices from FDBs for the selected listed companies on London Stock Exchange (LSE). In the forward analysis process, the comments are flagged using a predefined Pump and Dump financial crime related keyword template. By only flagging the comments against the keyword template yields an average of 9.82% potentially illegal comments. It is unrealistic and unaffordable for human moderators to read these comments on a daily basis in long run. Hence, by integrating the share prices’ hikes and falls to categorise the flagged comments based on risk levels, it saves time and allows relevant authorities to prioritise and investigate into the higher risk flagged comments as it can potentially indicate real Pump and Dump crimes on FDBs

Stem cell differentiation increases membrane-actin adhesion regulating cell blebability, migration and mechanics

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/K. S. is funded by an EPSRC PhD studentship. S.T. is funded by an EU Marie Curie Intra European Fellowship (GENOMICDIFF)

The long run relationship between private consumption and wealth : common and idiosyncratic effects

We investigate the long run relationship between private consump- tion, disposable income and wealth approximated by equity and house price indices for a panel of 15 industrialized countries. Consumption, income and wealth are cointegrated in their common components. The impact of house prices exceeds the effect arising from equity wealth. The long run vector is broadly in line with the life cycle permanent income hypothesis, if house prices are allowed to enter the relationship. At the idiosyncratic level, a long run equilibrium is detected between consumption and income, i.e. the wealth variable can be excluded. The income elasticity in the idiosyncratic relationship is significantly less than unity. Hence, the presence of wealth effects in consumption equations arises from the international integration of asset markets and points to the relevance of risk sharing activities of agents. Without sufficient opportunities, an increase in national saving rates would be expected, leading to a lower path of private consumption expenditures.info:eu-repo/semantics/publishedVersio

Land of Addicts? An Empirical Investigation of Habit-Based Asset Pricing Models

A popular explanation of aggregate stock market behavior suggests that assets are priced as if there were a representative investor whose utility is a power function of the difference between aggregate consumption and a “habit” level, where the habit is some function of lagged and (possibly) contemporaneous consumption. But theory does not provide precise guidelines about the parametric functional relationship between the habit and aggregate consumption. This makes for- mal estimation and testing challenging; at the same time, it raises an empirical question about the functional form of the habit that best explains asset pricing data. This paper studies the ability of a general class of habit-based asset pricing models to match the conditional moment restrictions implied by asset pricing theory. Our approach is to treat the functional form of the habit as unknown, and to estimate it along with the rest of the model’s finite dimensional parameters. This semiparametric approach allows us to empirically evaluate a number of interesting hypotheses about the specification of habit-based asset pricing models. Using stationary quarterly data on consumption growth, assets returns and instruments, our empirical results indicate that the estimated habit function is nonlinear, the habit formation is internal, and the estimated time-preference parameter and the power utility parameter are sensible. In addition, our estimated habit function generates a positive stochastic discount factor (SDF) proxy and performs well in explaining cross-sectional stock return data. We find that an internal habit SDF proxy can explain a cross-section of size and book-market sorted portfolio equity returns better than (i) the Fama and French (1993) three-factor model, (ii) the Lettau and Ludvigson (2001b) scaled consumption CAPM model, (iii) an external habit SDF proxy, (iv) the classic CAPM, and (v) the classic consumption CAPM

The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

When the target may know better: Effects of experience and information asymmetries on value from mergers and acquisitions

Ministry of Education, Singapore under its Academic Research Funding Tier