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

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... konulu sunumlar: "FINANCIAL MARKETS AND CAPITAL INSTUTIONS PHD FINANCE HÜSEYİN ÇETİN OKAN UNIVERSITY."— Sunum transkripti:

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 ratio developed by Nobel laurate William F.Sharpe to measure risk adjusted performance. The Sharpe ratio is calculated by subtracting risk free rate such as that of the 10 year U.S. Treasury bond from the rate of return for a portfolio and dividing the result by the standart deviation of portfolio returns.

11 Sharpe indicated that there can be correlation between financial asset prices and market index. He constructed regression model in order to proof 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 higher volatility compare to Fund B, Sharpe Ratio is used for Fund A and Return Analysis is done for Fund B.

15 SYSTEMATIC RISK Systematic risk is sort of market risk. By diversifying shares the risk can not be plummeted due to the external factors such as political movements,wars, international trade restriction, tax rate increases, inflation

16 UNSYSTEMATIC RISK That risk derives from internal problems of company. The risk can be minimized by the usage of statistical and mathematical methodologies. Unsystematic risk can be derived from the clash between shareholders and board of governors, contracts, auction win or loss.

17 BETA In CAPM, asset systematic risk is measured by Beta. Beta is equal to: covariance between market portfolio and financial asset divided by market portfolio variance. Beta equals to 1= middle risk group Beta smaller then 1=low risk group Beta bigger then 1= higher risk group

18 TREYNOR RATIO BETA RISK

19 ARBITRAGE PRICING MODEL At APT model, pricing is done by market participants. If there is a deviance from equilibrium price there will be arbitrage. Market participants get the asset in low price and wait the asset to become higher then sell the share back. With arbitraging strategy prices of asset can converge to equilibrium point.

20 FACTOR DEFINITION Research indicates that four basic factor can be significant to describe asset pricing -Unexpected change in inflation -Unexpected change in industrial production -Unexpected change in risk premiums -Unexpected change in short term and long term interest rates.

21 REGRESSION IN ARBITRAGE PRICING

22 ONE FACTOR ARBITRAGE MODEL

23 Investors short sells the X financial asset; at the same amount takes buys Y financial asset. At the first phase profit depends on expected Y return minus expected X return. Those buying and selling transaction decrease Y price. Until profits converges to zero, trade continues. To sum, financial assets which are at the same risk converges to same expected return.

24 JENSEN PERFORMANCE MEASUREMENT

25 JENSEN MEASUREMENT Jensen measurement takes Finansal Asset Market Line (FVPD) into account.

26 PORTFOLIO MANAGER PERFORMANCE JENSEN THRESHOLD a distance is Jensen distance. if a=0 portfolio manager does not have extra revenue from portfolio so extra revenue will not be taken. if a>0 Portfolio manager performance is above the expectation. So portfolio manager gains extra revenue. if a<0 Portfolio manager has poor performance in portfolio management and he can get warning from senior management.

27 EFFICIENCY FRONTIER

28 MARKOWITZ THEORY Markowitz argues that one portfolio return and risk can be correlated via Mean-Variance model. Within a particular return, via MV model, he minimizes the variance in portfolio and found the optimum portfolio theory.

29 Between 18 th November 2005 and 28 March 2008, 28 shares were used in the period of 509 days. MVS Model was the most successful model. Because with less risk investors can reach same return compare to MV and MVSE models who are higher risks but having same return with MVS model.

30 MVS model choosen

31

32 SHARES CORRELATION MATRIX

33 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.

34 NEURAL NETWORK ALGORITHM

35 VantagePoint Intermarket 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.

36 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.

37 VantagePoint Intermarket Analysis Software

38 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.

39 With VantagePoint Intermarket Analysis Software, for example, the raw data inputs involved in forecasting moving averages for euro FX futures include the daily open, high, low, close, volume and open interest for euro FX plus the daily open, high, low, close, volume and open interest data for nine related markets: · Australian dollar/U.S. dollar (AUD/USD) · Australian dollar/Japanese yen (AUD/JPY) · British pound · Euro/Canadian dollar (EUR/CAD) · Gold · Nasdaq 100 Index · British pound/Japanese yen (GBP/JPY) · British pound/U.S. dollar (GBP/USD) · Japanese yen

40 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

41 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.

42 REFERENCES okulu/portfoy-ve-senaryo-analizi/portfoy- optimizasyonu.aspx okulu/portfoy-ve-senaryo-analizi/portfoy- optimizasyonu.aspx lysis/journal_trading.asp lysis/journal_trading.asp gement.asp gement.asp -makale/markowitz-portfolio-theory-mean-variance- skewness-entropy-portfolio-selection


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