How to know in advance when stock market analysts are wrong?

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In one of my previous posts I show how simple averaging of opinions of analysts about future stock returns could significantly improve their predictions of market behavior. Still, much space for errors left, and almost all of it concentrated around market crashes. The latter is hard to predict, although there are some interesting approaches. Today I’ll use The CBOE Volatility Index (VIX®) to predict analyst error itself.
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When do financial analysts make mistakes?

While helping one of my students, Oleg Karapaev, in his struggle with a paper on determinants of divergence between “fair” and observed prices in stocks (written as a part of his bachelor research project), I’ve made some observations perhaps worth sharing with blogosphere. I used some R code and ggplot2ggthemes by Jeffrey Arnolds to represent findings visually (important: to replicate code in full you should have an access to Bloomberg and Rbbg package to download data).

Historical vs. Predicted price for Microsoft shares

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What impacts efficiency of pension fund managers in Russia (E.Fedorova, A.Didenko, D.Sedykh.)

We use data envelopment analysis (DEA) to assess efficiency of 46 pension fund managers in Russia during 2004-2012. Our DEA model represents pension fund portfolio as decision making unit transforming risk, human and financial capital to active return and quality of diversification. We find that the highest impact to efficiency of pension funds have stock market returns, while interest rates, corporate debts, FX rate have lower impact, and energy prices have no impact at all. Bigger and more mature portfolios with higher share of equities and cash would have lower returns. Seasonal factor impact is also high with third quarter being the toughest for managers. We explain it by scale effects and constraints implied by funds’ investment declarations.

(Published in Russian in «Financial analytics: science and experience», ISSN 2073-4484, issue No. 33(219) – 2014 September)

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Algotrading in Bloomberg, R, and beyond (Financial University, Spring-Fall 2014). 

Class description: introductory course on developing trading algorithms and algotrading industry as a whole. Class is held in financial lab with 9 Bloomberg terminals; students are learning to fast-prototype algorithms using R language and real data from markets. Focus is on practice, but good understanding of underlying theory is a must. Knowledge of Bloomberg platform and R language is an advantage.

Prerequisites: basics of financial markets, technical and fundamental analysis, financial mathematics, modern portfolio theory, statistics and probability theory.

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