通脹猛虎悄來 港息樓價何從？20220915

期價格平穩 會否一去不返？

全球性之通脹大潮，在過去兩年間令絕多數經濟學家及央行官僚跌盡眼鏡（還記得“暫時性”這個耳熟能詳的流行詞語否？）。就算今日，觀測家之共識仍然是「未來通脹會放緩」到新冠封城前、市場已於過去二、三十年來習以為常的0-3%水平。

然而，筆者展望通脹不但會高於市場預期，更會在高位持續更長時間。即是像1970年代上一次（同是）能源價格推動的通脹週期將會重臨。即使今次通脹率未必高過70年代時之高位，但亦將達到兩位數的水平，如圖：

How to better forecast Bitcoin prices? 20220909

Ever since bitcoin became a phenomenon way back in 2017 (some may even argue before then), there have been attempts to forecast its price trajectory into the future. The most common way of course was to use linear projection. But there are many other approaches that may get better results, and through our close and long standing analysis and investing in the asset, we provide here a very brief exposition of how price projections can be done for this, and perhaps all fast growing crypto assets.

 

There are logs, and then there are logs

Most excel wizards would be familiar with a handy feature to turn linear charting to logarithmic scale, which may provide much closer fit and easier for interpreting of trends. This is indeed how we started back in 2017 as well, and even now to many, the simple relationship still somewhat holds, despite some large deviations, especially at the early and late ends of the price spectrum:

Chart 1: BTC price trend on linear log scale - not a bad fit

However, as we found out ourselves over time, that simple tool became increasingly frustrating when the price of bitcoin began moderating from a linear fashion, this deviation became increasingly obvious after the 2018 bear correction.

We as a result, resorted to higher order polynomial log fitting to put the relationship back on a sound footing, starting with 3rd order polynomial log correlation. As one can see from the 2010-1 blue shaded areas in Chart 1, the gap was very large, but on 3rd order polynomial fitting, much reduced (see same shade in Chart 2). Further, the 2015-6 shaded area in Chart 1, although quite close to the actual price progression, was actually far from what a decaying trend should be, as seen in the distance between price and the 3rd order polynomial approach below:

Chart 2: BTC price trend on 3rd order polynomial log fitting – somewhat improved correlation

What is more, the average distance overtime between the trend line and actual price is now better at 69% compared to 168% using the linear approach. A pat on the back!

 

The story does not end here – as progressive price cycles set in – one can now count four cycle peaks and 4 cycle troughs from 2010 to date – the ability of the 3rd order polynomial approach to curve fit disappears. By adding orders of degree to the trend equation, we may play catch up, but that really is too mechanical and ultimately unwieldy, even though the degree of fit did improve (variance reading improved from 69% to 62%), here is 5^th order fitting:

Chart 3: BTC price trend on 5rd order polynomial log fitting – better fit still!

The advantage of another degree of freedom to capture turning points is illustrated above by the blue shaded area, where the trend now captures the 2022 downturn which was not identified by the 3^rd order trend in Chart 2.

Stock to flow model also has issues

Another approach that is well known out there is called the ‘stock-to-flow’ model where the value of bitcoin in circulation over the supply thereof over any set period is used as the basis of curve fitting to its traded price, as shown here:

Chart 4: Stock-to-Flow modelSource: https://buybitcoinworldwide.com/stats/stock-to-flow/

The validity or otherwise of this approach is not of interest to our discussion here, but there have certainly been very large deviations from actual traded price, as is obvious from the chart above, and often such deviations are sudden and unrelated to market conditions, driven by the halving phenomenon built into bitcoin’s protocol.

As a result of this, we decided to pursue other avenues to better model the price of bitcoin.

Commodity or currency, it is a monetary phenomenon

As all traders / investors might have had a hunch on how crypto price actions increasingly correlate with the wider capital markets, they may also begin to expect overall monetary conditions would have an increasing impact on price action of cryptos, especially now this asset class is valued in the trillions instead of mere billion or million dollars a few years ago.

In other words, we should increasingly factor in availability of money as an input variable for any projections of bitcoin prices. The other key variables we deem important in this calculation include also:
a) the number of non-zero balance bitcoin addresses, this acts as a proxy for the level of adoption in the population at large, and would be useful in modelling the network effect of rising usage of the asset;

b) number of BTC in issue, which obviously is increasing, but at a ever diminishing rate. In a sense, this metric is a bit like the money supply element of fiat currencies, where more supply debases the value and resulting in higher prices.

As an overall comparison, we put the three factors on the same scale so as to visualise the comparative impact they may have had on the price of Bitcoin in the recent past:

Chart 5: BTC price in the context of demand (# of addresses), supply (# of BTC in issue), and fuel (Global M3)

Obviously, some factors will have stronger influence over Bitcoin price at different phases of the asset’s history, eg the number of addresses featured heavily in the 2010-2 period when the growth was highest, while in the longer run, it may well be money supply which will dictate future price changes, when for example the user and supply growth both taper off.

 

In compiling the monetary facet of the input variables, we picked only the top 5 economies as proxy, but obviously more countries might produce better results. We will for now stay at the more manageable level this stage in our adventure. Here is the composition of the top 5 countries by M3:

Chart 6: top 5 countries by M3 (U$bn)

Perhaps surprisingly for most, China now has a bigger M3 value than the USA, and by quite a large margin, followed predictably by the EU and Japan. UK and India (not included) are much smaller by some distance, as shown in the chart above. Together, the top 5 M3 economies account for 55% of global GDP – may be at some stage we would bump that figure to 67% to increase the accuracy?

The new Grand Unified Theory of BTC?

With the above foundations, we are now ready to put all the pieces together – with a regression model curve fitting, we have come up with a new trend line for BTC prices, as shown here:

Chart 7: BTC price as predicted by our improved factor inputs

But sadly the variances of trend compared to actual prices were still some way off, until we increased the weighting of the M3 element (after all, money printing drives all fiat devaluation no?), to finally reach the more satisfying outcome shown here:

Chart 8: BTC price as predicted by our “grand unified theory” model

So now we have reduced the variance measured from the first version’s 66% to now 55% – still large by traditional asset standards – but we have some further refinement ideas already, and if we make enough progress, will share in a separate missive…

The advantages of the new methodology are multiple: a) it is not purely statistically driven; b) the model inputs are relevant and quantifiable; c) input variables allow for significantly more real world twists and turns in the fitted line which the statistical method is incapable of producing. Perhaps at some stage, this new grand unified theory will be useful as an input in our investment models…

We are definitely not finished in this quest for perfect modelling of Bitcoin prices, and there is a lot more work to be done, we leave you with a mystery chart that seems to do away with the problem of decaying exponential growth:

Chart 9: another elegant way to present linear BTC price trend

 

 

Is high inflation upon us? How will HK rates / home prices be affected? 20220825

Inflation may rise far more than expected 

With global inflationary surprises having caught most economists and central bankers off-guard over the past two years (“transitory” being the functional word), it is not hard to see why even now so many forecasts out there continue to point to moderation ahead; mostly back to the old norms before lockdowns, in the 0-3% that we have become so used to.

However, we argue that higher inflation will still beat expectations, and is likely to stay for longer still compared to popular expectations. In fact, a likely outcome might look like the 1970s when we had a previous (also energy price driven) inflationary cycle. Even a more modest trajectory that is above consensus but still below the 70s peak will take us into double-digit territory, looking something like this chart:

NowTV 財經新聞-訪問【業主稱暫冇太大壓力】1個月拆息10連升 普遍H按升穿2 5厘封頂水平 20220728

載於2022年7月27日  【NowTV財經新聞】【業主稱暫冇太大壓力】1個月拆息10連升 普遍H按升穿2.5厘封頂水平

 

原文: https://finance.now.com/news/post.php?id=744700

請前往作者YouTube頻道觀看更多視頻: 王震宇宇論 Yulun

 

英國樓價 大勢已去？[下篇]20220707

 英國樓價 大勢已去？[下篇]

本篇乃探討英國樓市投資利弊的結尾篇，以下較多專注剖析地緣政治和監管環境相關因素：

四) 窮兵黷武下民不聊生，資本外逃（往美國／亞洲）

烏克蘭衝突也許只是人類戰爭週期逆轉的延續，因為衝突傷亡人數趨勢線，已早於1990年代初於以世紀為單位的雙重支持位反彈，大有機會於未來時間急速抽升：

圖十七：衝突死亡人數可能已經觸底反彈

 

隨著1989年蘇俄共產集團垮台帶來的和平紅利逐漸成為遙遠的記憶，下一個噬血好戰的政治環境是否正在形成，將國際衝突的風險推向新高？而新經濟超級大國的崛起是否對現有秩序構成足夠的挑戰，而導致更多的磨擦？

當冷戰時期的意識形態之爭（即在共產主義和資本主義之間）已不復存在，下一個衝突的焦點會是什麼？會否未來的國際戰爭只是為了避免國內權力交替、純為轉移注意力而發動的？令人叫苦連天的高通脹和食品／能源短缺可會觸發下一場資源爭奪戰？

在舊秩序瓦解、多極世界來臨的劇變當前，投資者必須更加審慎。避免資金被困在衝突熱點國度之內（如歐洲，甚至北亞的某些地區——例如日本）。

可惜英國離其中一個高風險戰場太近，趁早由大不列顛獲利避險似乎並非壞事！此外，英國因為相對資源貧乏，在商品價格飛升的時代亦會吃虧，回報潛力將遠較筆者新推薦的自然資源優厚的投資目標遜色：

表一：歐洲＋亞洲嚴重依賴能源進口（紅色）反觀美洲＋大洋洲（黃色/綠色）則淨出口能源

 

五) 劫富不濟貧—拋售潮已經開始？

近月英國政府瘋狂沒收嚴格來講不屬政權級人物之私人財產（見：不分青紅皂白的沒收）可能成為有意投資英國之環球投資者的最後一根稻草。因為如此仇視私人產權的行政措施只有在戰時才會略有所聞。在沒有正當程序授權的情況下奪取俄裔人士的財產可能會全面摧毀英國作為一個安全投資地的聲譽，令人聯想起二戰期間日裔美國人遭無故囚禁（註），又或十月革命後俄共政權之共產行逕。

雖然俄國「極權政權」都放棄類似的掠奪行為，特別是彼方可輕而易舉地沒收數以倍計的在俄西方資產。英國卻帶頭在國際社會上於未正式宣戰（因要得到國會授權）或有法庭裁決的情況下沒收私產，這對倫敦未來吸引投資的能力來說確是個凶兆。

在如此背景下，李超人（即李嘉誠，香港首富）於2021年12月在僅獲25%微利之下將持有只夠4年的倫敦Broadgate以12.5億英鎊出售，似乎就不難解矣。長江集團著著先機的往績，更以2017年把香港The Centre以402億港元拆售而稱絕；當時嘲笑此交易的評論在在皆是，然而現在不少買家仍在舔著二至三成跌價的傷口。或許李先生對英國日益惡化的投資環境也有類似的評估？亦因此，不難理解為何長江進一步加大了處置英國投資的力度，將規模大得多的電力資產也在2022年3月以150億英鎊出售給麥格理和科爾伯格(KKR)（見此）。

可見本會留在英國的投資和職位現在將流向像迪拜之類的不那麼暴民政治，兼不政治掛帥的地方（見此文）。正如另一評論員所言：

儘管許多人為沒收邪惡的俄羅斯寡頭資產而歡呼，但其他國家將來亦會用同一戰略還治其身。…如果資產不再得到保護，那麼投資在西方國家還有何安全感？

西方政府在這場疫情中暴露了其並非不齒於脅迫與壓制言論自由的真面目（例如澳洲的宵禁、加拿大的銀行賬戶沒收、科網巨企抹去有爭議的醫學證據）。當一個城市或政權殞落時，富者總是最先逃離的一群。

這將徹底摧毀現有的世界經濟秩序。外國在俄羅斯的投資將被沒收，而資金逃往中國的機會極高。一條紅線被越過了。當政府以「為普京持有」為由去沒收私人資產，就有如聲稱任何生活與某領導人統治下之國民都在為該領導人持有資產。

以上轉變預示著大多數傳統西方城邦吸引財富的能力會出現結構性打擊，阿拉伯人、中國人、印度人，當然還有俄羅斯人未來的資金可能會另尋覓他方……

若要認識外國資本對倫敦市場的重要性，且看在截至2021年的11年中的增幅已可見端倪：

圖十八：土地註冊處海外個人業主紀錄——倫敦實是聚寶盆

數以十萬計認為英國是一個安全的資金歸宿地的業主現在可能會開始質疑並開始部署撤資，尤其是當針對外國業主反外情緒繼續升級時（就如【表二】般）。資本沒有忠誠，它只會流向最受優待的地方。

表二：英國不再是外資的避風港？

筆者擔心只要中國在烏克蘭衝突中站在與英國政治敘述不統一的角度，會否中國資金（包括港資）也遲早成為被制裁／沒收的目標？當政治立場成為了沒收私產的理由，那如果台灣問題會否觸發下一個地緣政治爭議？到時港人才撤資會否為時已晚？此一風險，是否就是令到李先生當下就套現英國投資的原因？

六) 規管限制無限膨脹，樓市投資已成雞肋

筆者早已在此詳細概述英國規章條款無窮無盡地攻擊業主權益；可惜在過去十年，繁多的合規要求從沒停止擴張，最新的例子包括：

甲) 就連出售房產都需要提供「反洗黑錢」（AML）聲明，如此與常理相悖的要求有何好處？以下是地產經紀被迫要放盤人大費周章填寫的表格：

表三：沒與沒了的表格和不斷加重的類似執法要求令業主叫苦連天

乙) 以下為業主會計師來信，皆因合規成本已經大到難以忽略，需要額外收費來應付的地步：

閣下會於本函見到「合規附加費」這新收費。您無疑亦有所知，所有專業服務商都面臨越來越多的監管和審查，處理如以下事項實需要大量額外的員工和第三方資源：

•確保新客開戶甚或監察舊客的反洗錢要求合規

•反賄賂和逃稅法規要求

•資料保護和GDPR要求

•日益頻繁的監管機構合規稽查訪問

•資訊科技與網絡安全

敝行已參詳向客戶收費的標準一段時間，發現以上成本增長速度超過通脹，已難以不分項徵收。

…因而另加2.5%的合規附加費，作為對這類支出之補貼。

丙) 綠色成本——新政策下過苛的能源性能證書(EPC)要求隨時會令業主破費1.3至2.7萬英鎊以求物業達標，詳情見此文；據《電訊報》報導，漢普頓斯經紀行估計英國東北區相關之升級工程已達該區年租金收入的83%；

丁) 商業樓宇亦無倖免——根據第一太平戴維斯，政府之「BEIS戰略框架」要求則更加嚴格：所有非住宅樓宇須於2023年前達到最低“乙”級評級，儘管87%的寫字樓現在EPC評級為“丙”級或以下。

戊) 稅收合規變得繁複——這是否預示更嚴苛的稅製即將來臨？以非居民業主向稅局申報為例光是決定是否可免繳英國稅項之測試標準已經令人不寒而慄：

 

這一年內：

你在海外有居所嗎？是/否

你在英國逗留幾天？

你與英國有多少聯繫**？

你在英國度過了多少個工作日？

你在海外度過了多少個工作日？

**「與英國的聯繫」定義如下（即2022年4月5日的年度）：

1你的配偶／伴侶／同居者或未成年子女居於英國

2在納稅年度內，你在英國於91天或以上有可用的住宿處所

並在那里至少住了一晚

3你在納稅年度在英國工作了40天或更長時間（3小時或更長時間）

4你在過去2個納稅年度中的任何一個納稅年度在英國停留超過90天

5你在英國度過的“午夜”比在任何其他國家都多

這一堆複雜扭曲的條件為任何稍為戀棧英國暑假，或者送兒女入學的訪客設置了一個代價高昂的陷阱，更毋庸說那些抵步經商的人了……

七) 脫歐前之移民湧入情景可能一去不返

英國於脫歐後人口增長應會顯著放緩，意味著原本非常緊張的住宅供應有可能逆轉；譬如歐盟公民班師回國可能已是一個長遠的負面因素：

圖十九：英國非常供應不足情況已經在脫歐後急速改善

此外，因疫情封鎖亦令遊客數量急跌（減少了對Airbnb／短租住宿的需求），再加上中國學生人數下降（地緣政治原因），可能會有因而釋放更多本來不會出現的供應。

綜上所述，筆者認為英國樓市未來將面臨更大的挑戰，投資者欲求可觀及低風險的回報亦須另謀他方。若要以最樂觀的假設來作一預測，在技術上還是有機會在未來數年出現樓價上漲14%的情景（圖二十中橙線），至於較悲觀的可能則會出現樓價下跌21%之結果（藍線）：

圖二十：到25年底技術分析區間：-21%至+14%

當然，不少港人出於資產配置，而非不停優化回報的原因而希望繼續持有英國物業。

茫茫天際 何去何從？

任何人只要將廿年前後的世界稍作比較，將不難得到「向東走」的答案【表四】；但如果中國風險溢價不是你那杯茶，那麼任何與中國經濟共同進退的市場（包括盛產大宗商品的國度）可能會是上佳的選擇......然而，在短期內，因債務危機而出現的走資朝（包括逃離歐洲甚至亞洲區域）意味著美國會是較穩妥的避難港。

表四：亞洲和商品出口國可能是未來跑贏大市的區域

 

本文早前出版的英文版本可在此細閱。

筆者特別鳴謝香港理工大學投資科學及金融分析系江譽展同學協助收集及整理本文相關數據及圖表