Témata prací (Výběr práce)Témata prací (Výběr práce)(verze: 336)
Detail práce
   Přihlásit přes CAS
At the right time, in the right factor. Can factors be timed?
Název práce v češtině: V pravý čas ve správném faktoru. Je možné faktory časovat?
Název v anglickém jazyce: At the right time, in the right factor. Can factors be timed?
Akademický rok vypsání: 2017/2018
Typ práce: bakalářská práce
Jazyk práce: angličtina
Ústav: Institut ekonomických studií (23-IES)
Vedoucí / školitel: Mgr. Martin Hronec
Řešitel: skrytý - zadáno vedoucím/školitelem
Datum přihlášení: 05.06.2018
Datum zadání: 05.06.2018
Datum a čas obhajoby: 10.09.2019 09:00
Místo konání obhajoby: Opletalova - Opletalova 26, O206, Opletalova - místn. č. 206
Datum odevzdání elektronické podoby:20.07.2019
Datum proběhlé obhajoby: 10.09.2019
Oponenti: RNDr. Michal Červinka, Ph.D.
Kontrola URKUND:
Seznam odborné literatury
Baba Yara, Fahiz and Boons, Martijn and Tamoni, Andrea, Value Timing: Risk and Return Across Asset Classes (March 9, 2018). Available at SSRN: https://ssrn.com/abstract=3054017 or http://dx.doi.org/10.2139/ssrn.3054017

Ang, Andrew, Factor Investing (June 10, 2013). Columbia Business School Research Paper No. 13-42. Available at SSRN: https://ssrn.com/abstract=2277397 or http://dx.doi.org/10.2139/ssrn.2277397

Hou, Kewei and Xue, Chen and Zhang, 张橹, Lu, Replicating Anomalies (June 12, 2017). Fisher College of Business Working Paper No. 2017-03-010; 28th Annual Conference on Financial Economics and Accounting; Charles A. Dice Center Working Paper No. 2017-10. Available at SSRN: https://ssrn.com/abstract=2961979 or http://dx.doi.org/10.2139/ssrn.2961979

Fama, E. F., & French, K. R. (1992). The cross section of expected stock returns. ‐ the Journal of Finance

Jensen, Michael C. and Black, Fischer and Scholes, Myron S., The Capital Asset Pricing Model: Some Empirical Tests. Michael C. Jensen, STUDIES IN THE THEORY OF CAPITAL MARKETS, Praeger Publishers Inc., 1972. Available at SSRN: https://ssrn.com/abstract=908569
Předběžná náplň práce v anglickém jazyce
Research question and motivation
Factors are attributes of an assets that are associated with possible higher returns. Factors are basically random variables created from the common characteristics of certain assets, such its volatility, its market capitalization, the value of its assets in comparison with market value or its momentum. These are a few of the most well-known and well documented factors. SMB (Small minus Big) factor represents a fact that small firms that have a small market capitalization tends to overperform the firms with high market capitalization. Analogically, HML (High minus low) factor represents a fact, that the firms which have a low book-to-market ratio tends to have higher returns than the firms with high book-to-market ratio. The logic is the same with other factors. Not surprisingly, there are times where certain factors are performing well and there are times where the same factors are performing very poorly. And since in practice many portfolios are created based on various factors, the natural question arises – Could the performance of the factors be predicted? Can we use certain factor in good times and avoid it in the bad times? In other words, could we time the factors? These questions will be tested and I will try to find the answers to them using various spreads between the characteristics constituting a factor. E.g. as Boons et al. (2018) did in their work Value timing.

This thesis follows the work of Boons et al. (2018) but will not dive so deep into one factor (Value) but it will rather include many factors and various forms of estimation of the respective factor premia. This different and more general approach can enrich the literature on factor investing and shed more light on the controversial topic of timing.

For data analysis the CRSP/Comupstat database will be used. It is a standard dataset in asset pricing literature which contains historical time-series data on more than 5000 stocks listed on NYSE and NASDAQ. Apart the historical data for each company Compustat data contains thousands of annual and quarterly income statements, balance sheets and cash flow statements. From these financial data various predictors will be constructed in order to analyse the variation of the returns of the specific factors.
Univerzita Karlova | Informační systém UK