In this paper we use the conditional Value at Risk (CoVaR) and CoVaR variation (ΔCoVaR) proposed by Adrian and Brunnermeier (2008, 2011, 2016) to estimate the Peruvian stock market risk (through the IGBVL) conditioned on the international financial market (given that the S&P500) and conditioned on three of the main commodities exported by Peru: copper, silver and gold. Moreover, the CoVaR measures are compared with the VaR of the IGBVL to understand the differences using conditional and unconditional risk measure estimators. The results show that both CoVaR and ΔCoVaR are useful indicators to measure the Peruvian stock market risk. / En este trabajo utilizamos el Valor en Riesgo condicional (CoVaR) y la variación CoVaR (ΔCoVaR) propuestos por Adrian and Brunnermeier (2008, 2011, 2016) para estimar el riesgo bursátil peruano (a través del IGBVL) condicionado en el mercado internacional (dado por el índice S&P500) y condicionado en tres de los principales comodities exportados por el Perú: cobre, plata y oro. Además, las medidas CoVaR son comparadas con el VaR del IGBVL para entender las diferencias al utilizar medidas de riesgo condicionales e incondicionales. Los resultados muestran que ambas medidas CoVaR and ΔCoVaR constituyen indicadores útiles para estimar el riesgo bursátil peruano.

Minorities hold a significantly smaller percentage of executive positions in companies within the S&P500. However, whether these minorities are under compensated relative to their non-minority counterparts has not been previously investigated. Using Compustat data, this paper documents the differences in compensation between minorities and non-minorities as a whole, minority and non-minority CEOs, and the differences in compensation for minorities and non-minorities within industries. I show that there is no minority/white wage gap overall, and in some cases, minorities earn a premium compared to non-minorities.

This thesis examines the application of neural networks in the context of option pricing. Throughout the thesis, different architecture choices and prediction parameters are tested and compared in order to achieve better performance and higher accuracy in option valuation. Two different volatility forecast mechanisms are used to compare neural networks performance with Black Scholes parametric model. Moreover, the performance of a neural network is compared also to more advanced modular neural networks. A new technique of adding rational prediction assumptions to neural network prediction is tested and the thesis shows the importance of adding virtual options fulfilling these assumptions in order to achieve better training of the neural network. This method comes out to increase the prediction power of the network significantly. The thesis also shows the neural network prediction outperforms the traditional parametric methods. The size and number of hidden layers in a neural network is tested with an emphasis to provide a benchmark and a structured way how to choose neural network parameters for future applications in option pricing. JEL Classification C13, C14, G13 Keywords Option pricing, Neural networks, Modular neu- ral networks, S&P500 index options Author's e-mail vach.dominik@gmail.com...

Investuotojai į akcijų rinkas ieško programinių būdų kaip analizuoti ir ranguoti akcijas, siekiant maksimalaus pelno. Analizės būdai skirstomi į dvi pagrindines grupes: fundamentalioji analizė ir techninė analizė. Šiame darbe autorius analizuoja esamus akcijų rangavimo būdus, įvairius indikatorius, o taip pat techninių ir fundamentaliųjų indikatorių apjungimo būdų, siekiant daryti geresnius sprendimus prekiaujant akcijų rinkoje. Ypatingas dėmesys darbe skiriamas rizikos valdymui, t.y. kokio dydžio pozicijos tikslingiausios sudarant akcijų portfelį. Darbe taip pat analizuojama kaip, taikant rizikos valdymo principus, ne tik pasiekti maksimalų pelną, bet ir svarbiausia neprarasi investuotų pinigų. Šio darbo objektas yra S&P500 indekso akcijų rangavimo algoritmai. Darbo tikslas yra sukurti ir eksperimentiškai išbandyti akcijų rangavimo algoritmus, naudojant tiek techninės, tiek fundamentaliosios mokyklų rodiklius. Teoriškai ir eksperimentiškai parodyti rizikos valdymo svarbą akcijų portfelio sudarymui. Pirmoje darbo dalyje analizuojami įvairūs akcijų rangavimo būdai ir programiniai algoritmai, didelis dėmesys skiriamas skirtingų rinkų sąveikai, o taip pat portfelio rizikos valdymui. Antroje dalyje nagrinėjama pasirinkta programinė įranga, aprašomi sukurti algoritmai, aprašomi sukurtos sistemos programiniai blokai, pateikiamos sistemos veikimo diagramos ir sprendimų medžiai. Trečioje darbo dalyje aprašomas S&P500 indekso akcijų eksperimentinis tyrimas, siekiant patikrinti... [toliau žr. visą tekstą] / Investors in stock markets are searching for computer software tools to analyze and rank stocks to make investment decisions. The analysis tools fall into two main groups: tools for fundamental analysis and tools for technical analysis of stock. In the current paper the author is looking for ways to combine fundamental and technical analysis to make better investment recommendations. Special emphasis is on money management rules used to open positions, calculate position size for each stock and overall portfolio value. Author also explains how to apply money management rules to not lose money in the markets. The object of this paper is S&P500 index stock ranking algorithms. The aim of the paper is to develop and test new stock ranking algorithms based on both technical and fundamental indicators. Money management rules is also a critical part selecting stocks and creating stock portfolios. In part 1 of the paper the reasons why each investor should you use both fundamental and technical analysis in his or her trading, and the importance of proper money management is explained. Author also writes about advantages using system trading instead of discretional trading. In part 2 of the paper the expert system is described. In part 3 of the paper the expert system is tested by analyzing S&P500 index stock and making trading decisions to form a stock portfolio. The expert system was tested for two weeks after is has been completed. Results of the system testing are analyzed and... [to full text]

This thesis analyses the impact of announcements of macroeconomic indicators in United States on price development of the VIX Futures, S&P500 Futures and EUR/USD FX rate. Theoretical part contains construction and description of individual markets. Empirical part investigates the reaction of market prices after 1, 10 and 30 minutes after announcement of an individual indicator value on a market surprise demonstrated as a difference between reported value and analysts' expectations. We tried to find a systematic reaction of market participants and the pace of absorption of new information into the market price. There have been found minimum of situations, where we explained the market move as a linear combination of market surprise. However, there was a several cases, where the market did not adjust to announced information quickly and was inefficient in a short period. In the second part of empirical research we tested all significant models on an out-sample data. The goal was to determine whether the market inefficiencies persisted and stable profit could be achieved. We analysed the brutto performance, then netto performance including all transaction costs. Finally, we defined a simple trading rules with a purpose of profit stabilization and lowering the riskiness of trades. For VIX Futures and EUR/USD markets we achieved a low loss, respectively negligible profit. For S&P 500 Futures we obtained a profit strategies for all selected indicators, total profit was high with a very low volatility of invested capital.

Macroeconomic forecasting is a classic problem, today most often modeled using time series analysis. Few attempts have been made using machine learning methods, and even fewer incorporating unconventional data, such as that from social media. In this thesis, a Generative Adversarial Network (GAN) is used to predict U.S. unemployment, beating the ARIMA benchmark on all horizons. Furthermore, attempts at using Twitter data and the Natural Language Processing (NLP) model DistilBERT are performed. While these attempts do not beat the benchmark, they do show promising results with predictive power. The models are also tested at predicting the U.S. stock index S&P 500. For these models, the Twitter data does improve the accuracy and shows the potential of social media data when predicting a more erratic index with less seasonality that is more responsive to current trends in public discourse. The results also show that Twitter data can be used to predict trends in both unemployment and the S&P 500 index. This sets the stage for further research into NLP-GAN models for macroeconomic predictions using social media data. / Makroekonomiska prognoser är sedan länge en svår utmaning. Idag löses de oftast med tidsserieanalys och få försök har gjorts med maskininlärning. I denna uppsats används ett generativt motstridande nätverk (GAN) för att förutspå amerikansk arbetslöshet, med resultat som slår samtliga riktmärken satta av en ARIMA. Ett försök görs också till att använda data från Twitter och den datorlingvistiska (NLP) modellen DistilBERT. Dessa modeller slår inte riktmärkena men visar lovande resultat. Modellerna testas vidare på det amerikanska börsindexet S&P 500. För dessa modeller förbättrade Twitterdata resultaten vilket visar på den potential data från sociala medier har när de appliceras på mer oregelbunda index, utan tydligt säsongsberoende och som är mer känsliga för trender i det offentliga samtalet. Resultaten visar på att Twitterdata kan användas för att hitta trender i både amerikansk arbetslöshet och S&P 500 indexet. Detta lägger grunden för fortsatt forskning inom NLP-GAN modeller för makroekonomiska prognoser baserade på data från sociala medier.

Gold price hits record high more than ¢C1900 in 2011, so how to forecast gold price and whether the influence factor of gold price change over time become more interesting issues for people. The beginning of this paper tries to find out the reasonable gold price then cut the study period into 7 stages and examines the influence factor of gold price in each stage from 1972 to 2011. Finally, this research uses the recent influence factor to build a forecasting model and tests its performance. The empirical result has three parts. First, from the view of purchasing power at December 31, 1971, gold price is too high in the end of 2011. Secondly, influence factors of gold price will change over time. They usually alter with important economic events of the world. Thirdly, the forecasting model has good performance in both in-sample and out-of-sample backtesting, but if the influence factor had changed, the performance would be worse in out-of-sample backtesting.

In this thesis, an analysis of Bitcoin, Monero price and volatility is conducted with respect to S&P500 and the VIX index. Moreover using Python, we computed correlation coefficients of nine cryptocurrencies with two different approaches: Pearson and Spearman from July 2016 -July 2018. Moreover the Pearson correlation coefficient was computed for each year from July2016 - July 2017 - July 2018. It has been concluded that in 2016 the correlation between the selected cryptocurrencies was very weak - almost none, but in 2017 the correlation increased and became moderate positive. In 2018, almost all of the cryptocurrencies were highly correlated. For example, from January until July of 2018, the Bitcoin - Monero correlation was 0.86 and Bitcoin - Ethereum was 0.82.

This thesis investigates the application of the Black-Scholes model for pricing long-maturity options, primarily utilizing historical data on S\&P500 options. It compares prices computed with the Black-Scholes formula to actual market prices and critically examines the validity of the Black-Scholes model assumptions over long time frames. The assumptions mainly focused on are the constant volatility assumption, the assumption of normally distributed returns, the constant interest rate assumption and the no transaction cost assumption. The results show that the differences between computed prices and actual prices decrease as options get closer to maturity. They also show that several of the Black-Scholes model assumptions are not entirely realistic over long time frames. The conclusion of the thesis is that there are several limitations to the Black-Scholes model when it comes to pricing long-maturity options.

本文著重在探討現股放空限制與交易成本對期貨錯價之影響。以門檻自我迴歸與續航門檻自我迴歸模型分析期貨錯價之非線性過程，我們發現錯價有回歸平均(mean reversion)的現象。當期貨錯價為正時(套利策略為買現貨賣期貨)，交易量對錯價影響為負；但若期貨錯價為負(套利策略為賣現貨買期貨)，考慮到昂貴的放空成本(costly short sell hypothesis)，交易量對錯價的影響將是較不明確的。 / This article highlights the impact of short selling restrictions and trading costs on the relation on futures mispricing error. Within threshold autoregression model (TAR) and momentum threshold autoregressive model (M-TAR), the influence of optimal arbitrage trading on the mispricing is analyzed. Results concerning trading volume and level, mean reversion in mispricing error, and the model which describes mispricing process better. The empirical evidence suggests that trading costs and short selling costs are influential factors for the mispricing behavior. Moreover, the futures trading volume affects mispricing level significantly.