In this
paper I explore the informational content in cross-currency flow
maintained by large custodian banks with an objective to design a
statistical arbitrage trading system
that could exploit such information. After an initial simple test
involving one-step ahead forecasts for JPYUSD FX pair with lagged I-Flow
data series and concluding that such forecasts don’t measure up to a
simple AR(1) model’s forecasts involving the FX pair time series itself,
I introduce a 15-day moving standard deviation variables based off the
I-Flow time series with a 5-day lag to the one-period I-Flow forecasting
model to discover a considerable improvement in forecasts to the
original model though still not bettering the simple AR(1) regression
model of the FX time series. With the information from the initial tests
at hand, we move on to explore the possibility of designing a system
to forecast the swings observed in the 15-day moving standard deviation
series of the JPYUSD FX pair. A partial dynamic equilibrium regression system
involving a transformation of the individual I-Flow series similar to
the FX pair series (15-Day moving standard deviation) is then
experimented with to capture long-run stable 5-period ahead forecast for
the 15-day moving standard deviation swings in the JPYUSD FX pair.
Finally, I propose two models to exploit the volatility swing forecastingsystem;
The first model involving volatility trades for the FX pair that could
exploit an expected upswing in the short term volatility of the FX pair,
and the second model involving an overlay of the swing forecastingsystem
over traditional trend-forecasts involving technical rules to capture
profitable long/short trades of the FX pair. Data between May 2007 and
May 2009 was employed for the exercise.

While a
great number of predictive variables for stock returns have been
suggested, their prediction power is unstable. We propose a Least
Absolute Shrinkage and Selection Operator (LASSO) estimator of a
predictive regression in which stock returns are conditioned on a large
set of lagged covariates, some of which are highly persistent and
potentially cointegrated. We establish the asymptotic properties of the
proposed LASSO estimator and validate our theoretical findings using
simulation studies. The application of this proposed LASSO approach to forecasting
stock returns suggests that cointegrating relationships among the
persistent predictors leads to a significant improvement in the
prediction of stock returns over various competing models in the mean
squared error sense.

A
Markov regime switching model for exchange rate fluctuations, with
time-varying transition probabilities, is used in constructing a monthly
model for predicting currency crises in Southeast Asia. The approach is
designed to avoid the estimation inconsistency that might arise from
misclassification errors in the construction of crisis dummy variables
which other approaches (such as probit/logit and signaling) require. Our
methodology also addresses the serial correlations and sudden behavior
inherent in crisis occurrence, identifies a set of reliable and
observable indicators of impending crisis difficulties, delivers
forecast probabilities of future crises over multi-period forecasting
horizons, and offers an empirical framework for analyzing contagion
effects of a crisis. Our empirical results indicate that the Markov
switching model is moderately successful at predicting crisis episodes,
but also points to future research in various directions. Most early
warning systems for currency crises have used either probit or
signaling. Several issues can be raised regarding these techniques: the
need for a priori dating of crisis occurrence, the use of arbitrary
thresholds, inadequate modeling of the dynamics in the system,
among others. We present an alternative framework, based on a
Markov-switching model of exchange rate fluctuations with time-varying
transition probabilities, which addresses these concerns.

seekers_: We
introduce Kinetic Component Analysis (KCA), a state-space application
that extracts the signal from a series of noisy measurements by applying
a Kalman Filter on a Taylor expansion of a stochastic process. We show
that KCA presents several advantages compared to other popular
noise-reduction methods such as Fast Fourier Transform (FFT) or Locally
Weighted Scatterplot Smoothing (LOWESS): First, KCA provides band
estimates in addition to point estimates. Second, KCA further decomposes
the signal in terms of three hidden components, which can be
intuitively associated with position, velocity and acceleration. Third,
KCA is more robust in forecasting
applications. Fourth, KCA is a forward-looking state-space approach,
resilient to structural changes. We believe that this type of
decomposition is particularly useful in the analysis of trend-following,
momentum and mean-reversion of financial prices.

An instrument
exhibits financial inertia when its price acceleration is not
significantly greater than zero for long periods of time. Our empirical
analysis of 19 of the most liquid futures worldwide confirms the
presence of strong inertia across all asset classes. We also argue that
KCA can be useful to market makers, liquidity providers and faders for
the calculation of their trading ranges.

The
purpose of this paper is to examine the problem faced by the portfolio
manager attempting to optimally incorporate forecasts of future market
returns into his portfolio. Given the solution to this problem we then
shall focus our attention on the problem involved in measuring a
portfolio manager's ability when he is explicitly engaged in forecasting the prices on individual securities (i.e., security analysis) and in forecasting
the future course of market prices (i.e., timing activities). We shall
consider these problems here in the context of the Sharpe-Lintner
mean-variance general equilibrium model of the pricing of capital
assets, and in the context of the expanded two factor version of the
Sharpe model suggested by Black, Jensen and Scholes (1972) In addition
we shall concentrate our attention here on an investigation of just what
can and cannot be said about portfolio performance solely on the basis
of data on the time series of portfolio and market returns.

In
section 2 we outline the foundations of the analysis and its
relationship to the general equilibrium structure of security prices
given by the Sharpe-Lintner model. In section 3 we briefly summarize the
measure of security selection ability suggested by Jensen (1968).
Section 4 contains a solution to the problem of the optimal
incorporation of market forecasts into portfolio policy and provides the
structure for the analysis in section 5 of the measurement problems
introduced into the evaluation of portfolio performance by market forecasting
activities by the portfolio manager. Section 6 presents the complete
development of the model within the two factor equilibrium model of the
pricing of capital assets suggested by Black (1970) and Black, Jensen
and Scholes (1972). Section 7 contains a brief summary of the
conclusions of the analysis.

Data
on individual trades in prediction markets relating to the 2008 and 2012
US Presidential elections reveal that traders vary enormously in their
behavior. This contrasts with the standard prediction-market models,
which assume relatively homogeneous participants who differ only in
their beliefs and wealth. We show that risk-lovers have particularly
strong distortionary effects on market outcomes even when beliefs are
symmetrically distributed around the truth. Simulations of a model which
allows traders to have different motives and tastes for risk indicate
that including such traders produce the market outcomes we observe, such
as herding, persistent contrariness, a skewed profits’ distribution and
favorite-long-shot bias. The attraction of such markets to risk-lovers
means that caution must be exercised when using prediction-market prices
for forecasting.

The present study is an attempt to evaluate the predictability of the
foreign exchange volatility in thirteen countries. The data covers the
period of 2005-2009. To effectively forecast the volatility in the
exchange rates, a GARCH model is used. The study compares the results
between crisis period and a set of normal periods. The empirical results
reveal that almost all countries except Thailand witnessed
non-existence of volatility shocks at least once in a three year
pre-crisis period but all the sample countries had volatility shocks in
the crisis period of 2008-09. This apparently indicates that forecasting
can be made at least for the next day given the high degree of
volatility in the crisis period. The paper also reveals that exchange
rates tend to have persistent conditional heteroskedasticity, and hence,
could be predicted with one lag term.

Thuesday 06 October 2016 at 07pm (US hour) the GBP and its ForEx
crosses, shows a massive SellOff, with moderate volume level and a
violent evolution in 10 minutes about. In fact there are only two 5min
bars on the chart and the hourly, daily and weekly frames shows the same
long-tail candle structure.
Some traders and analyst pointed out that the mini BlackSwan of the GBP
is a scandalous phenomenon in the normal dynamics of the financial
markets of the currencies.
The aim of this post is to search elements supporting or not this view.
Elements used ar very similar to previous post regarding BitCoin [#01]
large SellOff in 2014, and CHF [#02] historical BlackSwan of 2015.
☒ ITA Abstract.
In questo post si tenta di chiarire i violenti avvenimenti ribassisti
della Sterlina subiti il 6 ottobre 2016.
Tutti i dati raccolti (struttura grafica locale post-2009; grafico di
lungo termine; CoT; indicatore di instabilità politica UK; evento
BrExit) concordano in una evoluzione ribassista della Sterlina in
seguito alla rottura nel 2016 (BrExit) del livello critico 140. Il
target naturale sarebbe area 100 in caso di non recupero della base a
140.
Quindi i dati grafici, politici ed economici, mostrano che la rapida
evoluzione di prezzo della Sterlina durante il 6 ottobre 2016, risulta
solo una naturale evoluzione-conseguenza e non un evento scandaloso, e
inoltre non costituisce nemmeno un evento raro e isolato su base
mensile-settimanale. La fase post BrExit del prezzo della sterlina
(nuovi minimi al di sotto di 140; assenza di recupero) era un chiaro
segno di ulteriore debolezza in arrivo. Del resto i principali attori
CoT erano correttamente posizionati fin dal 2014-2015, non come accadde
sul CHF BlackSwan [#02].
☒ Graphical Elements.
The very long term structure of GBP index shows a large bearish pattern
(large red rectangle) with a first hard phase in 1981-1984 (''A''), a
very impulsive phase. A second phase (B1-B2-B3) as a large side action,
but well below (in average) vs. mid-level of the previous fast leg ''A''
(175-180). B3 top demarcates the restart of violent bearish action of
''A'' with a very similar behaviour until to 2009 lows. The next phase
is local blue side rectangle, pre-BrExit (see below).
Bearish of 2009 and 2016 broken down two important ascending rectangles
(blue trend lines), visibly breaking the multiannual attempt to recovery
the violent declines in ''80 years.
The level 140 about, for GBP Index future, is a very critical value on
very long term frame, according to some previous key lows (yellow
circles). Only during 1984-1985, GBP was below this critical level, with
a very fast and violent behaviour.
From the 2009 bottom (on 140 level about), there is a local side
structure (in blue) under bearish attack in 2016 (BrExit event), and
with possible bearish target at 100 (blue columns).
The BrExit event cause a monstre SellOff monthly candle, but not the
largest (see monthly bars of 1981, 1992, 2008), with bearish activation
of the side blue rectangle. Infact the level of 140 about was broken
down during June 2016 (BrExit), without a subsequet recovery, but with
new lows below 140.
The main bearish actions from 2008, shows fast increase and positive
spread between Commercial players vs. Large Traders on CoT curves (see
yellow lines). The top of this spread is in 2016.
The economic policy uncertainty index of UK, shows historical top values
also pre-BrExit (2016 first quarter). In the phase BrExit this index
obtain an unprecedented top of policy instability evaluations for UK.
☒ Attached Charts
http://bit.ly/2dJGJAI
http://bit.ly/2e3pZDc
http://bit.ly/2dqqLJC
☒ Notes.
All these data (local; long term; CoT; policy instability; BrExit event)
agree in a continuation-bear evolution of GBP until area 100. This
scenario is stopped if the GBP prices go above the base of the blue
rectangle 2009-2016; It is canceled if the GBP prices go above the
mid-line of the blue rectangle itself.
Graphical, political and economic data shows that the fast price
evolution of GBP of Thuesday 06 October 2016 is a natural evolution and
not a scandalous event; moreover, according to GBP long term chart, it
is not rare and isolate on monthly-weekly base.
The post BrExit phase of GBP (new lows below 140, recovery-less) was a
very clear sign of further weakness.
The CoT main players are correctly positioned from 2014-2015, not as in
CHF BlackSwan [#02].
☒ Chart sources.
❖ FinViz; St.LouisFED; TimingCharts.
Bibliography.
Salvatore Salvi Vicidomini, 2014 [#01] - Financial Markets Observatory
Lab. Some notes/charts about the intraday giant spikes of BitCoin
prices. - ForexFactory thread -
https://www.researchgate.net/publication/260159249
Salvatore Salvi Vicidomini, 2015 [#02] - Financial Markets Observatory
Lab. Notes and charts about the ForEx BlackSwan of Swiss Franc (CHF),
powered by Swiss National Bank in January 15, 2015, based on a
qualitative analysis of CoT curves. - ForexFactory thread -
http://www.researchgate.net/publication/271137015

Financial
planning can be define as a group of plans to get necessary financing
resources and use it. That's mean identify the financial needs in
specific period. And we most know that a financial planning one of most
important elements of organization financing strategy, because it
convert this strategy to practical steps we can apply it. Financial
planning depends on financial forecasting which basically depend on
organizations sales, maybe one question come on our minds, why we chose
the sales to financial forecasting?. Because it has a relation with
organizations assets and liabilities.to descript the relation between
sales and asset we can said that organization increase assets when it
tries to increase the sales. In other words when organization starts
product, it needs to increase the material inventory which is necessary
for production and to increase the sales. In addition, the organization
sales will not be necessary in cash, it can be on credit, that refers to
increasing in organizations credit balance. From other side the
organization need to increase the financing resources to accomplish the
increasing sales goal, which shows to us the relation between sales and
liabilities. Financial forecasting by using the sales percentage method;
1. Identify the budget accounts which is changeable with the sales. 2.
Find the budget accounts percentage by divide it on the recently sales
(S). 3. Collecting the asset and liabilities percentage (A/s & L/s).
4. Find the E.F.R (External funds required) by using incoming formula;
E.F.R = (S 2-S 1) (A/S – L/S)-(MBS 2). E.F.R: External funds required. S
1: recently sales. S 2 : forecasted sales. A/S : total asset percentage
that is changeable with the sales. L/S: total liabilities percentage
that changeable with the sales. M: marginal profit percentage. B:
returned earning percentage. 5. Now regroup the budget accounts
percentage in new budget (forecasted budget). 6. Multiply each account
percentage by forecasted sales sum. 7. Regroup the budget account that
isn't changeable with the sales in forecasted budget. 8. We most edit
returned earning sum, by add the (MBS 2) sum to the returned earning
balance. 9. Add E.F.R sum with liabilities in forecasted budget to
balance both credit and debt side.

This paper propose that the combination of smoothing approach taking
into account the entropic information provided by Renyi method, has an
acceptable performance in term of forecasting errors. The methodology of
the proposed scheme is examined through benchmark chaotic time series,
such as Mackay Glass, Lorenz, Henon maps, the Lynx and rainfall from
Santa Francisca series, with addition of white noise by using neural
networks-based energy associated (EAS) predictor filter modified by
Renyi entropy of the series. In particular, when the time series is
short or long, the underlying dynamical system is nonlinear and temporal
dependencies span long time intervals, in which this are also called
long memory process. In such cases, the inherent nonlinearity of neural
networks models and a higher robustness to noise seem to partially
explain their better prediction performance when entropic information is
extracted from the series. Then, to demonstrate that permutation
entropy is computationally efficient, robust to outliers, and effective
to measure complexity of time series, computational results are
evaluated against several non-linear ANN predictors proposed before to
show the predictability of noisy rainfall and chaotic time series
reported in the literature.

Files:Files:Files:seekers_:We introduce Kinetic Component Analysis (KCA), a state-space application that extracts the signal from a series of noisy measurements by applying a Kalman Filter on a Taylor expansion of a stochastic process. We show that KCA presents several advantages compared to other popular noise-reduction methods such as Fast Fourier Transform (FFT) or Locally Weighted Scatterplot Smoothing (LOWESS): First, KCA provides band estimates in addition to point estimates. Second, KCA further decomposes the signal in terms of three hidden components, which can be intuitively associated with position, velocity and acceleration. Third, KCA is more robust in forecasting applications. Fourth, KCA is a forward-looking state-space approach, resilient to structural changes. We believe that this type of decomposition is particularly useful in the analysis of trend-following, momentum and mean-reversion of financial prices.An instrument exhibits financial inertia when its price acceleration is not significantly greater than zero for long periods of time. Our empirical analysis of 19 of the most liquid futures worldwide confirms the presence of strong inertia across all asset classes. We also argue that KCA can be useful to market makers, liquidity providers and faders for the calculation of their trading ranges.

In section 2 we outline the foundations of the analysis and its relationship to the general equilibrium structure of security prices given by the Sharpe-Lintner model. In section 3 we briefly summarize the measure of security selection ability suggested by Jensen (1968). Section 4 contains a solution to the problem of the optimal incorporation of market forecasts into portfolio policy and provides the structure for the analysis in section 5 of the measurement problems introduced into the evaluation of portfolio performance by market forecasting activities by the portfolio manager. Section 6 presents the complete development of the model within the two factor equilibrium model of the pricing of capital assets suggested by Black (1970) and Black, Jensen and Scholes (1972). Section 7 contains a brief summary of the conclusions of the analysis.

Files:Files:Files:Files:Files:This paper propose that the combination of smoothing approach taking into account the entropic information provided by Renyi method, has an acceptable performance in term of forecasting errors. The methodology of the proposed scheme is examined through benchmark chaotic time series, such as Mackay Glass, Lorenz, Henon maps, the Lynx and rainfall from Santa Francisca series, with addition of white noise by using neural networks-based energy associated (EAS) predictor filter modified by Renyi entropy of the series. In particular, when the time series is short or long, the underlying dynamical system is nonlinear and temporal dependencies span long time intervals, in which this are also called long memory process. In such cases, the inherent nonlinearity of neural networks models and a higher robustness to noise seem to partially explain their better prediction performance when entropic information is extracted from the series. Then, to demonstrate that permutation entropy is computationally efficient, robust to outliers, and effective to measure complexity of time series, computational results are evaluated against several non-linear ANN predictors proposed before to show the predictability of noisy rainfall and chaotic time series reported in the literature.Files: