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Welcome To This Finance Blog. You Will Probably Like It If You Are Interested In Making Money From Stocks And Currency Markets Or You Enjoy Reading General Money Topics.

Sunday, August 05, 2018

The 10 Best Paying Industries in Kenya


           The 10 Best Average Monthly Pay
                                                                    

v  Extra territorial bodies *UN, WTO, World Bank   Kshs. 249,978

v  Finance and Insurance                                               Kshs. 139,793

v  Administrations and Support Service                        Kshs. 116,156

v  Transport and Storage                                                 Kshs.100,285

v  Scientific and Technical Activities                             Kshs. 83,053

v  Education                                                                 Kshs. 74,707

v  Information and Communication                               Kshs. 72,842

v  Arts, Entertainment and Recreation                            Kshs. 50,086

v  Health and Social Work                                                Kshs. 63,688

v  Construction                                                               Kshs. 52,601


Adapted from KNBS ECONOMIC SURVEY 2017

Monday, May 28, 2018

My Trading Approach and Guidelines





When I did my first live trade in Jan 2012, scalping as portrayed in forums seemed the dependable approach to making infinite returns. The very first trade was accidental. While I was acquainting myself with the trading terminal, I pressed the new order button and before I could learn its contents I did a trade without knowing the volume, pair or the price. Excitement had led me to ignore demo and go straight to live account. The first conscious trade I bought USDCHF and closed the position immediately it started sliding. Looking back I understood the ‘cut your losses’ to literally mean never tolerate floating loss. Believe you me I repeated such similar trades for the next six months till my account hit zero dollars. Of course during the six months and later on I tried all sort of approaches flaunted in the internet. I had progressed from the simple moving averages to the so-said advanced Elliott Wave Analysis. However, after blowing up I realized there was more to this forex market than meets the eye. The colorful Metatrader charts, dogmatic indicators and trading ‘psychology’ related articles presented low quality knowledge about the market. And I still hold this view till to date.

So what is my approach? The belief that quality knowledge about market is not found in promotional articles inspired me to aim for a more research oriented trading approach. My approach emphasizes formulating testable ideas, reading academic literature related to the idea and testing the idea before trading it live. I thought “because I am about to start an economics statistics bachelor’s degree, the approach will be complemented by whatever concepts I learn in class”. Class work helped a bit but mostly it did not turn out as I had expected (why? Story for another day...). However, even after undergrad not meeting my expectations I still stick to the core approach of formulate, test and then trade! 

The hard work and enjoyable part has been learning programming languages and tools that can help test the ideas or replicate academic papers. Started with the MQL5 language, later learned Matlab and now I combine both MQL5 and Matlab to test and automate ideas.

 Below are the main themes and respective rationale that guide trading decisions:

v  Entries And Stops Are Based On Time Rather Than Prices

v  A Low Frequency Of Trading; Usually 12 Main Trade Ideas In One Year

v  Long Positions Only. Rarely Will I Sell

v  Develops Mean Reversion Rather Trend Following Systems

v   Trade positions with positive swaps. Swaps generate a significant portion of the returns

v  Disapprove trade ideas generated by the system using stylized statistical facts collected over time

v  Continuously formulate and test new hypothesis


Thank you for reading 


Wednesday, May 23, 2018

Too much Empiricism in financial Markets




 Think of anytime you have set out to research on a certain stock, currency. Usually, the first step we take is to plot and explore the historical prices. Often we are stuck in doing all sort of analysis on the past prices because quantitative data, ‘big data’, ’numbers’ is the magical source of credibility in financial analysis. This believe that knowledge can only be acquired through past experiences is called Empiricism. Empiricism is effective but has some limitations especially when we over depend on it to answer questions related to human behavior.

 ‘Numbers don’t lie’ is a big lie in some cases. Let’s start with creative accounting. It takes advantage of shareholders faith in numbers, so whatever numbers the accountants feed the investor public, they will probably believe it. Although such accounting would best be described as outright fraud rather than empiricism, it demonstrates the problem of underdetermination and bias that exist in empiricism.  Given the same dataset, two analysts will come up with stories that align with their interest using the same data. If truth is universally objective, then how can same data points produce different conclusions? Then probably one of the analyst or both are using numbers to lie.

Because empirical finance inherently assumes the future will be similar to the past, it suffers the problem of induction. The classical example of a farmer and the chicken says a chicken that receives grains each morning will assume that is the norm until Christmas day when the farmer will slaughter the chicken instead of feed it. Value at risk, volatility models, AR and ARMAs etcetera all this common finance tools seek to fit the past and project to the future but they do not warn us when ‘Christmas day’ is approaching.

So what is the way forward? Empiricism is great and probably the most effective philosophical approach to carrying out research however it has to be used in conjunction with other ways of discovering knowledge. The money culture by Michael Lewis describes some of the best deal makers in the ‘80s that relied on alternative sources of knowledge rather than historical hard data. Personally, I am unashamedly empiricist just like most finance researchers, traders however I believe combining quant data, common sense (intuition) and perspectives from smart people can yield good results.