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FINANCIAL MARKETS AND CAPITAL INSTUTIONS

FINANCIAL MARKETS AND CAPITAL INSTUTIONS. PHD FINANCE HÜSEYİN ÇETİN OKAN UNIVERSITY. PORTFOLIO MANAGEMENT DEFINITION. The art and science of making decisions about investment mix and policy, matching investments to objectives , asset allocation for individuals and

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FINANCIAL MARKETS AND CAPITAL INSTUTIONS

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  1. FINANCIAL MARKETS AND CAPITAL INSTUTIONS PHD FINANCE HÜSEYİN ÇETİN OKAN UNIVERSITY

  2. PORTFOLIO MANAGEMENT DEFINITION • The art and science of making decisions about investment mix and policy, matching investments to objectives, asset allocation for individuals and institutions, and balancing risk against performance. Portfolio management is about strengths,weaknesses, opportunities and threats in the choice of debt vs. equity, domestic vs. international, growth vs.safety, and many other tradeoffs encountered in the attempt to maximize return at a given appetite for risk (Investpodia)

  3. ACTIVE AND PASSIVE PORTFOLIO MANAGEMENT • There are two forms of portfolio management: passive and active. Passive management simply tracks a market index, commonly referred to as indexing or index investing. Active management involves a single manager, co-managers, or a team of managers who attempt to beat the market return by actively managing a fund's portfolio through investment decisions based on research and decisions on individual holdings.

  4. ADL MATRIX

  5. Groupama Emeklilik A.Ş. E.Y. Fonları • Büyüme amaçlı olan bu fon, portföyünün en az %80'ini İMKB'de işlem gören hisse senetlerine yatırarak sermaye kazancı elde etmeyi amaçlar. Portfoyün geri kalan kısmı ise ters repo veya devlet tahvili içermektedir. • Strateji: Fon portföyüne likiditesi fazla , büyüme potansiyeli yüksek, sektöründe geleceği olan şirketlerin hisse senetlerine yatırım yapılarak sermaye kazancı elde edilmesi amaçlanmaktadır. • Fon portföyünün büyük bir kısmı hisse senetlerinden oluştuğu için makro ekonomik risk, sektör riski, firma riski ve likidite riski taşımaktadır. Fon yönetiminde risklerden korunmak amacı ile çeşitlendirme yapılmakta, riskler dağıtılarak asgariye indirilmektedir. • Yatırımcı Profili: Hisse senedi riski almak isteyen ve yüksek getiri hedefleyen, agresif risk profiline sahip yatırımcılara uygun olan emeklilik yatırım fonudur.

  6. Ziraat Yatırım Menkul Değerler A.Ş. Yatırım Fonları • Yüksek oranda hisse senedi taşıyan,risk ve getiri düzeyi yüksek bir fondur. Hisse senetleri ağırlıklı olarak IMKB 30 ve kısmen de IMKB 100 den seçilmektedir. Ziraat Yatırım Menkul Değerler A.Ş nin kurucusu olduğu yatırım fonları içinde risk düzeyi en yüksek olan yatırım fonudur. Önerilen yatırım süresi minimum 9 aydır.

  7. Yatırımcının başlangıç yatırımının belirli bir bölümünün, tamamının ya da başlangıç yatırımının üzerinde belirli bir getirinin izahnamede belirlenen esaslar çerçevesinde belirli vade ya da vadelerde yatırımcıya geri ödenmesinin, uygun bir yatırım stratejisine dayanılarak en iyi gayret esası çerçevesinde amaçlandığı ve şemsiye fon şeklinde kurulan fonlar “KORUMA AMAÇLI FON” olarak adlandırılır.

  8. Anapara Koruma Amaçlı Yatırım Fonları yatırımcılara, anaparalarının koruma altında olduğu bir ortamda, farklı yatırım enstrümanlarına yatırım yaparak getiriye ortak olma şansı tanır. Anapara koruma amaçlı yatırım fonları, genellikle 6 ay veya daha uzun vadelidir. Satış işlemleri belirli dönemlerde halka arz yöntemiyle yapılır. Halka arz döneminden sonra fona yeni giriş yapılamazken, fondan çıkışlar belirli koşullar altında genellikle mümkündür.

  9. EFFICIENT FRONTIER

  10. SHARPE RATIO • A ratiodevelopedby Nobel laurate William F.Sharpetomeasure risk adjustedperformance. TheSharperatio is calculatedbysubtracting risk free rate such as that of the 10 year U.S. Treasurybondfromthe rate of returnfor a portfolioanddividingtheresultbythe standart deviation of portfolioreturns.

  11. Sharpeindicatedthatthere can be correlationbetweenfinancialassetpricesand market index. He constructedregression model in ordertoproof his theory. • ri = ai + b(m) + i • ri : Finansal varlık getirisi • ai : Regresyon sabiti • bi : Finansal varlık getirisinin piyasa getirisine olan hassasiyeti (sistematik riskin ölçüsü • olan beta katsayısı) • r(m) : Piyasa (endeks) getirisi • i : Hata terimi (finansal varlığın, piyasa getirisinden bağımsız, sistematik olmayan riski)

  12. SHARPE RATIO RISK ADJUSTMENT • The Sharpe ratio tells us whether a portfolio's returns are due to smart investment decisions or a result of excess risk. This measurement is very useful because although one portfolio or fund can reap higher returns than its peers, it is only a good investment if those higher returns do not come with too much additional risk. The greater a portfolio's Sharpe ratio, the better its risk-adjusted performance has been.

  13. SHARPE RATIO

  14. FUND A AND FUND B • Since Fund A has highervolatilitycomparetoFund B, SharpeRatio is usedforFund A and Return Analysis is done forFund B.

  15. SYSTEMATIC RISK • Systematic risk is sort of market risk. Bydiversifyingsharesthe risk can not be plummetedduetotheexternalfactorssuch as politicalmovements,wars, internationaltraderestriction, tax rate increases, inflation

  16. UNSYSTEMATIC RISK • That risk derivesfrominternalproblems of company. The risk can be minimizedbytheusage of statisticalandmathematicalmethodologies. Unsystematic risk can be derivedfromtheclashbetweenshareholdersand board of governors, contracts, auctionwinorloss.

  17. BETA • In CAPM, assetsystematic risk is measuredby Beta. Beta is equalto: covariancebetween market portfolioandfinancialassetdividedby market portfoliovariance. Beta equalsto 1= middle risk group Beta smallerthen 1=low risk group Beta biggerthen 1= higher risk group

  18. TREYNOR RATIO BETA RISK

  19. ARBITRAGE PRICING MODEL • At APT model, pricing is done by market participants. Ifthere is a deviancefromequilibriumpricetherewill be arbitrage. Market participantsgettheasset in lowpriceandwaittheassettobecomehigherthenselltheshareback. Witharbitragingstrategyprices of asset can convergetoequilibriumpoint.

  20. FACTOR DEFINITION • Researchindicatesthatfourbasicfactor can be significanttodescribeassetpricing • Unexpectedchange in inflation • Unexpectedchange in industrialproduction • Unexpectedchange in risk premiums • Unexpectedchange in shorttermandlongterminterestrates.

  21. REGRESSION IN ARBITRAGE PRICING

  22. ONE FACTOR ARBITRAGE MODEL

  23. Investorsshortsellsthe X financialasset; at thesameamounttakesbuys Y financialasset. • At thefirstphaseprofitdepends on expected Y returnminusexpected X return. • Thosebuyingandsellingtransactiondecrease Y price. Untilprofitsconvergestozero, tradecontinues. • Tosum, financialassetswhichare at thesame risk convergestosameexpectedreturn.

  24. JENSEN PERFORMANCE MEASUREMENT

  25. JENSEN MEASUREMENT • JensenmeasurementtakesFinansal Asset Market Line(FVPD) intoaccount.

  26. PORTFOLIO MANAGER PERFORMANCE JENSEN THRESHOLD • a distance is Jensendistance. if a=0 portfoliomanagerdoes not haveextrarevenuefromportfoliosoextrarevenuewill not be taken. if a>0 Portfolio managerperformance is abovetheexpectation. Soportfoliomanagergainsextrarevenue. if a<0 Portfolio manager has poorperformance in portfoliomanagementand he can getwarningfromseniormanagement.

  27. EFFICIENCY FRONTIER

  28. MARKOWITZ THEORY • Markowitzarguesthatoneportfolioreturnand risk can be correlatedviaMean-Variance model. Within a particularreturn, via MV model, he minimizesthevariance in portfolioandfoundthe optimum portfoliotheory.

  29. Between 18 thNovember 2005 and 28 March 2008, 28 shareswereused in theperiod of 509 days. MVS Model wasthemostsuccessful model. Becausewithless risk investors can reachsamereturncompareto MV and MVSE modelswhoarehigherrisks but havingsamereturnwith MVS model.

  30. MVS model choosen

  31. SHARES CORRELATION MATRIX

  32. MONTE CARLO SIMULATION • MCS is a technique that converts uncertainties in input variables of a model into probability distributions. By combining the distributions and randomly selecting values from them, it recalculates the simulated model many times and brings out the probability of the output.

  33. NEURAL NETWORK ALGORITHM

  34. VantagePointIntermarket Analysis Software • The first network forecasts tomorrow’s high to help set stops for entry and exit points. • The second network forecasts tomorrow’s low to help set stops for entry and exit points. • The third network forecasts a 5-day moving average of closes two days into the future to indicate the expected short-term trend direction within the next two days. • The fourth network forecasts a 10-day moving average of closes four days into the future to indicate the expected medium-term trend direction within the next four days. • The fifth network indicates whether the market is expected to change trend direction within the next two days, by making a top or a bottom.

  35. The first four networks at the primary level of the network hierarchy make independent market forecasts of the high, low, short-term trend and medium-term trend. These predictions are then used as inputs into the fifth network, along with other intermarket data inputs, at the secondary level of the network hierarchy, to predict market turning points.

  36. VantagePointIntermarket Analysis Software

  37. Neural networks provide the data from intermarket analysis that can be used to produce predicted moving averages for a few days ahead. The blue line is the predicted 10-day moving average, the black line the actual 10-day moving average. Note that the blue line often turns ahead of the black line, giving traders an early alert to get into or out of a position before the crowd.

  38. WithVantagePointIntermarket Analysis Software, forexample, theraw data inputsinvolved in forecastingmovingaveragesforeuro FX futuresincludethedailyopen, high, low, close, volumeandopeninterestforeuro FX plusthedailyopen, high, low, close, volumeandopeninterest data for nine relatedmarkets: • · Australiandollar/U.S. dollar (AUD/USD) • · Australiandollar/Japanese yen (AUD/JPY) • · British pound • · Euro/Canadiandollar (EUR/CAD) • · Gold • · Nasdaq 100 Index • · British pound/Japanese yen (GBP/JPY) • · British pound/U.S. dollar (GBP/USD) • · Japanese yen

  39. TAIWAN STOCK EXCHANGE NEUREAL NETWORK ANALYIS • The Pearson correlation tested the relationship between stock returns and each of the nine financial variables: market capitalization, dividend yield, P/S ratios, P/B ratios, price-to-cash flow ratios, short-term rate of return, long-term rate of return, turnover rate and earning to price ratios. The dependent samples (paired samples) t-test investigated the differences between predicted stock returns (created through the neural networks by using financial ratios and behavioral finance proxies) and actual stock returns, and compared the mean of monthly predicted stock returns with the mean of monthly actual returns within different industries

  40. The results showed that all nine factors except the price-to-cash flow ratio related significantly with stock returns and helped explain average stock returns in the Taiwan stock market during the 10 year testing period (1999–2008). Financial ratios (market capitalization, dividend yield, P/S ratio, and P/B ratio) and behavioral finance proxies (short-term rate of return, long-term rate of return, turnover rate, and E/P ratio) proved to be important determinants of stock returns. The paired samples t-test results indicated that the predicted stock returns based on fundamental analysis approximated actual returns in the traditional industry.

  41. REFERENCES • http://www.ziraatportfoy.com.tr/yatirimci-okulu/portfoy-ve-senaryo-analizi/portfoy-optimizasyonu.aspx • http://vp.tradertech.com/lbm_library/intermarket_analysis/journal_trading.asp • http://www.investopedia.com/terms/p/portfoliomanagement.asp • http://gradworks.umi.com/33/74/3374769.html http://www.arastirmax.com/bilimsel -makale/markowitz-portfolio-theory-mean-variance- skewness-entropy-portfolio-selection

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